query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Finds all complete figurate cycles for given svalues. | def figurate_cycles(*s_vals):
assert len(s_vals) > 1 #incomplete sanity check
# Since a DFS has to start SOMEWHERE and we're looking for cycles, we
# arbitrarily take the first list of figurates and use them as the
# roots of our search.
roots = figurate_list(s_vals[0])
# Make a big list of all ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_all_cycles(s,graph):\n\n grph = u.edge_to_list_dict(graph)\n node_cnt = len(grph)\n k = z.Int(\"k\")\n syms = [z.Int('node%s'%i) for i in range(node_cnt)]\n\n # s.add(syms[0] == 0) # start node is a 0\n s.add(k < node_cnt)\n s.add(k > 1)\n\n o = z.Optimize()\n\n #... | [
"0.6175425",
"0.57670933",
"0.5475402",
"0.53936297",
"0.5329402",
"0.522589",
"0.52258706",
"0.5211429",
"0.51233554",
"0.5099929",
"0.5068521",
"0.5063437",
"0.5032627",
"0.5028338",
"0.49907324",
"0.4966183",
"0.49148342",
"0.49041694",
"0.48915786",
"0.4890928",
"0.488326... | 0.7913429 | 0 |
Depthfirst search for cycle finding | def find_all_cycles(candidates, new_elem, path=[]):
def have_cycle(candidates, path):
""" Checks that when we have no more candidates, that our path
'endpoints' are cyclical. """
return (not candidates and path[0].prefix == path[-1].suffix)
def have_dead_end(candidates, new_ele... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depthFirstSearch(problem):\r\n \"*** YOUR CODE HERE ***\"\r\n node = problem.getStartState()\r\n if (problem.isGoalState(node)):\r\n return [] # no need to make any moves of the start state is goal\r\n start = (node, 'NoDirection',0)\r\n\r\n frontier_queue = Stack() # queue for frontier\r\n frontier_q... | [
"0.72399324",
"0.7194816",
"0.7168289",
"0.71484697",
"0.7130135",
"0.70507145",
"0.7048199",
"0.7036242",
"0.70055723",
"0.7004626",
"0.69954205",
"0.69783103",
"0.69622517",
"0.6950441",
"0.6899392",
"0.68852323",
"0.6877258",
"0.68730253",
"0.6850732",
"0.68145895",
"0.679... | 0.0 | -1 |
Checks that when we have no more candidates, that our path 'endpoints' are cyclical. | def have_cycle(candidates, path):
return (not candidates and path[0].prefix == path[-1].suffix) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_paths(self):\n for path in self.paths:\n # check that arc starts at s\n arc = path[0]\n arc_start = self.arc_info[arc][\"start\"]\n assert(arc_start == self.source()), \"Path does not start at s\"\n # check that internal arcs are valid\n ... | [
"0.64591026",
"0.64028496",
"0.62922645",
"0.6263719",
"0.62504596",
"0.6250248",
"0.62022024",
"0.6092906",
"0.60610884",
"0.6022989",
"0.6006082",
"0.5956604",
"0.595369",
"0.5885716",
"0.58702284",
"0.58571595",
"0.5837033",
"0.5828162",
"0.5810018",
"0.5783266",
"0.576298... | 0.6560611 | 0 |
Checks that we have at least one candidate whose prefix is cyclical with the new element's suffix. | def have_dead_end(candidates, new_elem):
return new_elem.suffix not in map(lambda x: x.prefix, candidates) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def have_cycle(candidates, path):\n return (not candidates and path[0].prefix == path[-1].suffix)",
"def find_all_cycles(candidates, new_elem, path=[]):\n \n def have_cycle(candidates, path):\n \"\"\" Checks that when we have no more candidates, that our path\n 'endpoints' are cyclical... | [
"0.688956",
"0.67497593",
"0.6492469",
"0.58144426",
"0.57607526",
"0.57607526",
"0.57380426",
"0.5663673",
"0.56448567",
"0.55977595",
"0.5587944",
"0.55387944",
"0.55290985",
"0.54872364",
"0.5447168",
"0.5441908",
"0.5441635",
"0.5411554",
"0.5400539",
"0.53535",
"0.532922... | 0.69614404 | 0 |
Returns a new list where all sgonal candidates have been removed. | def remove_sgons(s_value, candidates):
return list(filter(lambda x: x.s != s_value,
candidates)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup():\n for s in [missiles, explosions, bonus]:\n\n set_to_remove = set([])\n for m in s:\n if m.isDead:\n set_to_remove.add(m)\n\n s.difference_update(set_to_remove)",
"def rm(x, l):\n return [y for y in l if x != y]",
"def removed_vms(self) -> Lis... | [
"0.62687725",
"0.6201206",
"0.61726826",
"0.6111378",
"0.60760725",
"0.6075531",
"0.6048856",
"0.59979814",
"0.59737307",
"0.59074605",
"0.59073585",
"0.5861006",
"0.58275676",
"0.58228827",
"0.5821122",
"0.58096284",
"0.57756793",
"0.577101",
"0.5767358",
"0.57616895",
"0.57... | 0.718452 | 0 |
Grows and then returns a binary decision tree. | def growDecisionTreeFrom(rows, evaluationFunction=entropy):
if len(rows) == 0: return DecisionTree()
currentScore = evaluationFunction(rows)
bestGain = 0.0
bestAttribute = None
bestSets = None
columnCount = len(rows[0]) - 1 # last column is the result/target column
for col in range(0, co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_tree(rows: list) -> DecisionNode or Leaf:\n info_gain, question = get_best_split(rows)\n\n # If no info is gained just return a leaf node with remaining rows\n if info_gain == 0:\n return Leaf(rows)\n\n true_rows, false_rows = partition(rows, question)\n false_branch = build_tree(fa... | [
"0.73607105",
"0.7327917",
"0.72056544",
"0.69596493",
"0.69034684",
"0.68098634",
"0.6802638",
"0.6777719",
"0.6634053",
"0.65713704",
"0.64563805",
"0.64558756",
"0.64070386",
"0.63814086",
"0.63564616",
"0.62751037",
"0.6235292",
"0.620463",
"0.61548716",
"0.6144888",
"0.6... | 0.6963232 | 3 |
Prunes the obtained tree according to the minimal gain (entropy or Gini). | def prune(tree, minGain, evaluationFunction=entropy, notify=False):
# recursive call for each branch
if tree.trueBranch.results == None: prune(tree.trueBranch, minGain, evaluationFunction, notify)
if tree.falseBranch.results == None: prune(tree.falseBranch, minGain, evaluationFunction, notify)
# merge ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prune( tree, impurity_crit, dataSet, treeSeq ):\n\n\t\tsaved = {}\n\n\t\ttotal_leaf_impurity, num_leaves = DecisionTree._fetch(tree, impurity_crit, dataSet, saved)\n\n\t\tnodes, sets, G = saved['node'], saved['set'], saved['G']\n\n\t\t# choose TreeNode such that g is minimum to prune\n\t\tmin_g_ind = np.argmi... | [
"0.71780753",
"0.6755928",
"0.6264333",
"0.6245853",
"0.61987823",
"0.61671454",
"0.6129602",
"0.6123983",
"0.6105711",
"0.60467637",
"0.603748",
"0.599225",
"0.5975164",
"0.5917185",
"0.5786919",
"0.5713195",
"0.5691192",
"0.5679927",
"0.56610376",
"0.5647163",
"0.56247056",... | 0.7445959 | 0 |
Classifies the observationss according to the tree. | def classify(observations, tree, dataMissing=False):
def classifyWithoutMissingData(observations, tree):
if tree.results != None: # leaf
return tree.results
else:
v = observations[tree.col]
branch = None
#if isinstance(v, int) or isinstance(v, float)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classify(observation,tree):\n if tree.results!=None:\n return tree.results\n else:\n v=observation[tree.col]\n branch=None\n if isinstance(v, int) or isinstance(v, float):\n if v>=tree.value:\n branch=tree.tb\n else: \n branc... | [
"0.64260566",
"0.628646",
"0.6179566",
"0.61025393",
"0.6020093",
"0.5886732",
"0.58653593",
"0.58054006",
"0.5720676",
"0.5713302",
"0.5651006",
"0.553406",
"0.5490271",
"0.5489133",
"0.5437679",
"0.5431709",
"0.53708494",
"0.52685785",
"0.5266328",
"0.5201875",
"0.51812655"... | 0.60514915 | 4 |
Plots the obtained decision tree. | def plot(decisionTree):
def toString(decisionTree, indent=''):
if decisionTree.results != None: # leaf node
return str(decisionTree.results)
else:
if isinstance(decisionTree.value, int) or isinstance(decisionTree.value, float):
decision = 'Column %s: x >= %s?... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_decision_tree(classifier, feature_names=None, class_names=None):\n fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(4, 4))\n tree.plot_tree(\n classifier,\n feature_names=feature_names,\n class_names=class_names,\n rounded=True,\n filled=True,\n )\n fig.sh... | [
"0.7786561",
"0.7618409",
"0.7559046",
"0.73626286",
"0.7209401",
"0.70228016",
"0.6984339",
"0.67858964",
"0.6662463",
"0.66269195",
"0.6623892",
"0.6618223",
"0.65958834",
"0.65787435",
"0.65618527",
"0.6497789",
"0.64806896",
"0.6466314",
"0.6460015",
"0.6435375",
"0.63971... | 0.7129504 | 5 |
Loads a CSV file and converts all floats and ints into basic datatypes. | def loadCSV(file):
def convertTypes(s):
s = s.strip()
try:
return float(s) if '.' in s else int(s)
except ValueError:
return s
reader = csv.reader(open(file, 'rt'))
return [[convertTypes(item) for item in row] for row in reader] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadCSV(input_file):",
"def load_csv():\n df = pd.read_csv(datafolder+filename, decimal=decimal).astype(\n {'min': 'float', 'max': 'float'})\n return df",
"def place_types_read_csv(self, csv_input):\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''])\n ... | [
"0.7278124",
"0.7128511",
"0.7073164",
"0.6921024",
"0.69140124",
"0.6835938",
"0.6834292",
"0.68103707",
"0.6777402",
"0.6776886",
"0.674985",
"0.67280674",
"0.6687896",
"0.6687714",
"0.66312677",
"0.6607438",
"0.65860206",
"0.6575137",
"0.65615463",
"0.6559472",
"0.6544173"... | 0.7568201 | 0 |
Ban an ip from all DDNet servers. Minutes need to be greater than 0. | async def global_ban(self, ctx: commands.Context, ip: str, name: str, minutes: int, *, reason: clean_content):
await self._global_ban(ctx, ip, name, minutes, reason) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ban_host(self, host, hard=False, duration=None):\n # TODO: Timed bans?\n logger.verbose(\"Banning IP {0}\".format(host))\n self.ip_bans.add(host, hard)",
"def ban_all():\n sudo(\"varnishadm 'ban req.url ~ .'\")",
"def test_exclude_ip_ban(self):\n pass",
"def ban_ip(self, ip... | [
"0.7068072",
"0.68500394",
"0.61785233",
"0.6124741",
"0.60522395",
"0.5987351",
"0.5956545",
"0.581452",
"0.5770357",
"0.5694431",
"0.55829155",
"0.5578588",
"0.55710304",
"0.5567556",
"0.5565699",
"0.5553093",
"0.5491359",
"0.5439812",
"0.5364005",
"0.5349952",
"0.533009",
... | 0.5913455 | 7 |
Ban an ip from all DDNet servers in given region. Minutes need to be greater than 0. Region needs to be the 3 char server code. | async def global_ban_region(self, ctx: commands.Context, region: str, ip: str, name: str, minutes: int, *, reason: clean_content):
await self._global_ban(ctx, ip, name, minutes, reason, region) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_exclude_ip_ban(self):\n pass",
"def ban_host(self, host, hard=False, duration=None):\n # TODO: Timed bans?\n logger.verbose(\"Banning IP {0}\".format(host))\n self.ip_bans.add(host, hard)",
"async def global_unban(self, ctx: commands.Context, *, name: str):\n if re.m... | [
"0.5752577",
"0.5619576",
"0.5450576",
"0.54234755",
"0.5233336",
"0.5208012",
"0.51359165",
"0.51315814",
"0.5063948",
"0.4974001",
"0.4927776",
"0.48956954",
"0.48733237",
"0.48646176",
"0.4827561",
"0.48271024",
"0.48046353",
"0.4773166",
"0.47669205",
"0.47156936",
"0.471... | 0.6312553 | 0 |
Unban an ip from all DDNet servers. If you pass a name, all currently globally banned ips associated with that name will be unbanned. | async def global_unban(self, ctx: commands.Context, *, name: str):
if re.match(r'^[\d\.-]*$', name) is None:
query = 'SELECT ip FROM ddnet_bans WHERE name = $1;'
ips = [r['ip'] for r in await self.bot.pool.fetch(query, name)]
if not ips:
return await ctx.send(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unban(self, ctx, name: str):\n try:\n bans = await self.bot.get_bans(ctx.message.server)\n user = discord.utils.get(bans, name=name)\n if user is not None:\n await self.bot.unban(ctx.message.server, user)\n except discord.Forbidden:\n ... | [
"0.69601077",
"0.62606454",
"0.62166715",
"0.5990166",
"0.5937297",
"0.59223866",
"0.5848375",
"0.5733963",
"0.5713095",
"0.5705937",
"0.57039034",
"0.5630587",
"0.56287026",
"0.5598197",
"0.55778",
"0.5549266",
"0.55139863",
"0.547942",
"0.54626197",
"0.5460259",
"0.5442353"... | 0.8504455 | 0 |
Show all currently globally banned ips | async def global_bans(self, ctx: commands.Context):
admin_cog = self.bot.get_cog('Admin')
query = """SELECT ip, name, to_char(expires, \'YYYY-MM-DD HH24:MI\') AS expires, reason, mod, region
FROM ddnet_bans ORDER BY expires;
"""
await admin_cog.sql(ctx, query=q... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getBanIps(self):\n banned = []\n q = \"\"\"SELECT clients.ip as target_ip FROM penalties INNER JOIN clients ON penalties.client_id = clients.id\n WHERE penalties.type = 'Ban' AND penalties.inactive = 0 AND penalties.time_expire = -1\n GROUP BY clients.ip\"\"\"\n ... | [
"0.72004414",
"0.6599594",
"0.657093",
"0.6570719",
"0.6473562",
"0.6393452",
"0.63146794",
"0.60255736",
"0.59439915",
"0.58867484",
"0.58601767",
"0.58601767",
"0.5859019",
"0.58225703",
"0.58142954",
"0.5804886",
"0.5790678",
"0.57876307",
"0.5770729",
"0.5734063",
"0.5714... | 0.63579273 | 6 |
Returns the internal identifier of the managed folder, which is a 8character random string, not to be confused with the managed folder's name. | def id(self):
return self.odb_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_id(self):\r\n buildfile_relpath = os.path.dirname(self.address.buildfile.relpath)\r\n if buildfile_relpath in ('.', ''):\r\n return self.name\r\n else:\r\n return \"%s.%s\" % (buildfile_relpath.replace(os.sep, '.'), self.name)",
"def directory_id(self) -> pulumi.Output[str]:\n ... | [
"0.65704453",
"0.646924",
"0.642653",
"0.63661224",
"0.6347965",
"0.6327185",
"0.63253933",
"0.62791514",
"0.62302345",
"0.6227704",
"0.6227704",
"0.62033147",
"0.61708695",
"0.6147371",
"0.6134929",
"0.6116909",
"0.6108815",
"0.6105652",
"0.6101452",
"0.607058",
"0.607058",
... | 0.0 | -1 |
Delete the managed folder from the flow, and objects using it (recipes or labeling tasks) | def delete(self):
return self.client._perform_empty(
"DELETE", "/projects/%s/managedfolders/%s" % (self.project_key, self.odb_id)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, flow):\n for parent in self.parents:\n parent.children.remove(self)\n for child in self.children:\n child.parents.remove(self)\n\n flow.blocks.remove(self)",
"def delete(self):\n\n del self.parent_mirror_dir[self.cvs_path]",
"def delete(self):\n ... | [
"0.63252056",
"0.62532264",
"0.62450886",
"0.61435694",
"0.6130529",
"0.60856485",
"0.607105",
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"0.5986059",
"0.5971468",
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"0.58870596",
"0.58690304",
"0.5862892",
"0.5856957",
"0.58366543",
"0.58277... | 0.5729521 | 32 |
Get the definition of this managed folder. The definition contains name, description checklists, tags, connection and path parameters, metrics and checks setup. | def get_definition(self):
return self.client._perform_json(
"GET", "/projects/%s/managedfolders/%s" % (self.project_key, self.odb_id)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_definition(self):\n return self.definition",
"def get_definition(self):\n return self.client._perform_json(\n \"GET\", \"/admin/groups/%s\" % self.name)",
"def definition(self):\n\n return self._definition",
"def definition(self):\n\n return self._definition",
... | [
"0.5817693",
"0.57296735",
"0.5583368",
"0.5583368",
"0.5496289",
"0.5493704",
"0.5425934",
"0.5191441",
"0.51887023",
"0.51887023",
"0.51505697",
"0.5133599",
"0.5114542",
"0.511077",
"0.510388",
"0.50844294",
"0.5081377",
"0.5072677",
"0.5038703",
"0.5038015",
"0.50089884",... | 0.65869796 | 0 |
Set the definition of this managed folder. | def set_definition(self, definition):
return self.client._perform_json(
"PUT", "/projects/%s/managedfolders/%s" % (self.project_key, self.odb_id),
body=definition) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def definition(self, definition):\n\n self._definition = definition",
"def set_definition(self, definition):\n return self.client._perform_json(\n \"PUT\", \"/admin/groups/%s\" % self.name,\n body = definition)",
"def _set_definition(self, definition: Dict[str, Any]):\n ... | [
"0.6675459",
"0.6660227",
"0.63897157",
"0.6099818",
"0.60483444",
"0.5964043",
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"0.5540315",
"0.55372936",
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"0.5355961",
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"0.53073615",
"0.5296438",
"0.5226685",
"0.52230346",
"0.52160645",
"0.5206463",
"0.5166973",
"0.515407... | 0.7764521 | 0 |
Get the list of files in the managed folder | def list_contents(self):
return self.client._perform_json(
"GET", "/projects/%s/managedfolders/%s/contents" % (self.project_key, self.odb_id)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getFiles(self):\n\t\treturn os.listdir(self.getPath())",
"def listFiles(self):\n pass",
"def list_dir(self, path):",
"def get_files(self):\r\n return self._filelist",
"def list_files():\n try:\n return jsonify(os.listdir(env(\"FILES_DIRECTORY\"))), 200\n except:\n return {... | [
"0.7972349",
"0.7716443",
"0.7584789",
"0.7555709",
"0.73637474",
"0.7340221",
"0.73126596",
"0.7310499",
"0.72796893",
"0.7199038",
"0.71473455",
"0.7116592",
"0.7082018",
"0.7078788",
"0.7074433",
"0.7053219",
"0.70311123",
"0.6994967",
"0.6977119",
"0.6969476",
"0.6964744"... | 0.0 | -1 |
Get a file from the managed folder | def get_file(self, path):
return self.client._perform_raw(
"GET", "/projects/%s/managedfolders/%s/contents/%s" % (self.project_key, self.odb_id, utils.quote(path))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fs_get_file(url, working_dir):\n if not os.path.isabs(url) and working_dir:\n url = os.path.join(working_dir, url)\n\n try:\n with codecs.open(url, 'r', encoding='utf-8') as f:\n return f.read()\n except Exception as e:\n raise ScrBaseExcept... | [
"0.6983217",
"0.6982181",
"0.67835194",
"0.67477846",
"0.6745828",
"0.6731098",
"0.66658807",
"0.6599749",
"0.6590214",
"0.654747",
"0.65451306",
"0.65048134",
"0.6465553",
"0.64533436",
"0.64236987",
"0.6402294",
"0.6359911",
"0.6332184",
"0.6332184",
"0.63053685",
"0.629940... | 0.75442284 | 0 |
Delete a file from the managed folder | def delete_file(self, path):
return self.client._perform_empty(
"DELETE", "/projects/%s/managedfolders/%s/contents/%s" % (self.project_key, self.odb_id, utils.quote(path))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, filename):\n pass",
"def delete_file(file_id):\n file_obj = Data.objects.get(id=file_id)\n print(\"Removing file: \", file_obj.name)\n print(file_obj.file.path)\n file_dir = file_obj.file.path\n os.remove(file_dir)\n print(\"Done.\")",
"def delete(self, filename, **kw)... | [
"0.77171427",
"0.7711893",
"0.7555799",
"0.7529862",
"0.7435636",
"0.7412828",
"0.7405208",
"0.7381543",
"0.7283083",
"0.72761863",
"0.72573847",
"0.7255518",
"0.7244785",
"0.7224803",
"0.721601",
"0.7209082",
"0.718607",
"0.7183738",
"0.7181639",
"0.7163021",
"0.7153897",
... | 0.7846849 | 0 |
Upload the file to the managed folder. If the file already exists in the folder, it is overwritten. | def put_file(self, path, f):
return self.client._perform_json_upload(
"POST", "/projects/%s/managedfolders/%s/contents/%s" % (self.project_key, self.odb_id, utils.quote(path)),
"", f).json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def put_upload(self):\n # print \"starting upload...\", self.current_upload['filepath']\n self.touch()\n self.log(\"STARTING_UPLOAD\", level=INFO)\n try:\n Backend.put_file(self.fileobj, self.current_upload[\"gcs_url\"])\n except exceptions.FilePutError as err:\n ... | [
"0.74072707",
"0.7129274",
"0.70463955",
"0.6660341",
"0.65970427",
"0.6557907",
"0.6512261",
"0.65012866",
"0.64963216",
"0.6455349",
"0.64418864",
"0.64405924",
"0.64309657",
"0.6395738",
"0.63393605",
"0.6332386",
"0.63101023",
"0.62682",
"0.62641364",
"0.6259874",
"0.6209... | 0.63176036 | 16 |
Upload the content of a folder to a managed folder. | def upload_folder(self, path, folder):
for root, _, files in os.walk(folder):
for file in files:
filename = os.path.join(root, file)
with open(filename, "rb") as f:
rel_posix_path = "/".join(os.path.relpath(filename, folder).split(os.sep))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload(self, folder, recursive=True, test=False):\n return self._gphotocli_image_tasks.upload(folder, recursive, test)",
"def UploadFolderToGD(token_path, source_path, gd_folder): \n google_drive = ConnectGoogleDrive(token_path)\n file_cmd = spike.FileCMD()\n file_list = file_cmd.ListFiles(so... | [
"0.6675317",
"0.65023863",
"0.65004486",
"0.63931644",
"0.6374531",
"0.6330987",
"0.62475437",
"0.6244524",
"0.61936384",
"0.6177249",
"0.61518073",
"0.6149765",
"0.6138441",
"0.6122861",
"0.6103507",
"0.60236096",
"0.6016508",
"0.60020953",
"0.59947294",
"0.5948904",
"0.5944... | 0.7008945 | 0 |
Compute metrics on this managed folder. | def compute_metrics(self, metric_ids=None, probes=None):
url = "/projects/%s/managedfolders/%s/actions" % (self.project_key, self.odb_id)
if metric_ids is not None:
return self.client._perform_json(
"POST" , "%s/computeMetricsFromIds" % url,
body={"me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_metrics(self):\n pass",
"def compute_statistics(self):",
"def collect_metrics():\n p = os.path.join(os.sep, \"mnt\", \"glusterfs\")\n mount_stats = os.statvfs(p)\n # block size * total blocks\n total_space = mount_stats.f_blocks * mount_stats.f_bsize\n free_space = mount_stats... | [
"0.7508439",
"0.6544122",
"0.6464246",
"0.6412694",
"0.6407469",
"0.6334957",
"0.6284569",
"0.62502366",
"0.62502366",
"0.6228933",
"0.6222924",
"0.6189192",
"0.6135421",
"0.60870016",
"0.6076569",
"0.6038834",
"0.60206956",
"0.601785",
"0.5966371",
"0.59614676",
"0.59115523"... | 0.607636 | 15 |
Get the last values of the metrics on this managed folder. | def get_last_metric_values(self):
return ComputedMetrics(self.client._perform_json(
"GET", "/projects/%s/managedfolders/%s/metrics/last" % (self.project_key, self.odb_id))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last(self):\n data = self._http_get(\"last\")\n return data.json()",
"def getLatestSpectrumMeasurements(self): \n return self.spectrum[len(self.spectrum)-1]",
"def getLatestMeasurement(self): \n return self.measurement[len(self.measurement)-1]",
"def last_value(s... | [
"0.64085966",
"0.63444847",
"0.63409215",
"0.62631094",
"0.62495613",
"0.62179077",
"0.61889714",
"0.61730325",
"0.6146713",
"0.61448973",
"0.6101925",
"0.6096707",
"0.6094985",
"0.6079614",
"0.60533327",
"0.60533327",
"0.6048",
"0.6036222",
"0.6022216",
"0.594147",
"0.593541... | 0.80391484 | 0 |
Get the history of the values of a metric on this managed folder. | def get_metric_history(self, metric):
return self.client._perform_json(
"GET", "/projects/%s/managedfolders/%s/metrics/history" % (self.project_key, self.odb_id),
params={'metricLookup' : metric if isinstance(metric, str) or isinstance(metric, unicode) else json.dumps(metric)}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_value_history(self):\n return self.value_history",
"def get_history(self):\n return self.history",
"def history(self):\n return self.info['history']",
"def get_history(self):\n return self.__history[:]",
"def get_history(self):\r\n\r\n return self.board_history",
"d... | [
"0.7165081",
"0.7030458",
"0.6927986",
"0.68705124",
"0.6810044",
"0.6718102",
"0.6691567",
"0.6691567",
"0.6686829",
"0.6673763",
"0.6653554",
"0.6653554",
"0.65635055",
"0.65367013",
"0.65315855",
"0.6509732",
"0.64998275",
"0.6481372",
"0.64430577",
"0.6438002",
"0.6354089... | 0.77961314 | 0 |
Get the flow zone of this managed folder. | def get_zone(self):
return self.project.get_flow().get_zone_of_object(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zone(self):\n return self._zone",
"def access_zone(self):\n return self._access_zone",
"def zone(self) -> str:\n return self._zone",
"def zone(self) -> str:\n return pulumi.get(self, \"zone\")",
"def zone(self) -> str:\n return pulumi.get(self, \"zone\")",
"def loca... | [
"0.64082503",
"0.6311886",
"0.6157333",
"0.6036838",
"0.6036838",
"0.59640765",
"0.59602046",
"0.5879423",
"0.58616424",
"0.58191",
"0.5772731",
"0.5772731",
"0.5696067",
"0.56114745",
"0.55910367",
"0.55511653",
"0.55193275",
"0.54935896",
"0.5492465",
"0.54857844",
"0.54575... | 0.75278246 | 0 |
Move this object to a flow zone. | def move_to_zone(self, zone):
if isinstance(zone, basestring):
zone = self.project.get_flow().get_zone(zone)
zone.add_item(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_stage_to_z(self, z):\n raise NotImplementedError",
"def move(self):\n pass",
"def move(self):\n raise NotImplementedError",
"def move_to(self, mobject_or_point):\n layer_center = self.surrounding_rectangle.get_center()\n if isinstance(mobject_or_point, Mobject):\n ... | [
"0.6282164",
"0.6204604",
"0.5991178",
"0.5983192",
"0.59183925",
"0.5749237",
"0.5678627",
"0.5613564",
"0.557024",
"0.5564908",
"0.55558306",
"0.5479993",
"0.5476635",
"0.54687375",
"0.545523",
"0.54463863",
"0.54068005",
"0.53963697",
"0.53693956",
"0.53560627",
"0.5351554... | 0.72328943 | 0 |
Share this object to a flow zone. | def share_to_zone(self, zone):
if isinstance(zone, basestring):
zone = self.project.get_flow().get_zone(zone)
zone.add_shared(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_to_zone(self, zone):\n if isinstance(zone, basestring):\n zone = self.project.get_flow().get_zone(zone)\n zone.add_item(self)",
"def flow(self, flow):\n\n self._flow = flow",
"def update_flow(self, flow):\r\n self.flow = flow",
"def transfer(self):\n pass... | [
"0.5932415",
"0.59072703",
"0.5562259",
"0.55534965",
"0.54047054",
"0.53833073",
"0.536776",
"0.5333624",
"0.5330987",
"0.5305724",
"0.52923506",
"0.5281795",
"0.5245011",
"0.52386606",
"0.52386606",
"0.52386606",
"0.5230287",
"0.5215504",
"0.516134",
"0.5156009",
"0.5132243... | 0.7893951 | 0 |
Unshare this object from a flow zone. | def unshare_from_zone(self, zone):
if isinstance(zone, basestring):
zone = self.project.get_flow().get_zone(zone)
zone.remove_shared(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unpossessed(self):\r\n self.owner = None",
"def unblock(self, source):\n raise NotImplementedError",
"def unShare(sharedItem):\n sharedItem.store.query(Share, Share.sharedItem == sharedItem).deleteFromStore()",
"def unassign_instance(InstanceId=None):\n pass",
"def __del__(self):\n ... | [
"0.601934",
"0.59649634",
"0.59522724",
"0.57343334",
"0.5674939",
"0.56622416",
"0.5578648",
"0.55039036",
"0.5499391",
"0.54815376",
"0.5437976",
"0.5432895",
"0.54314196",
"0.542576",
"0.53983927",
"0.5378879",
"0.5366127",
"0.53503096",
"0.53274274",
"0.5304955",
"0.52901... | 0.8114609 | 0 |
Get the recipes referencing this folder. | def get_usages(self):
return self.client._perform_json("GET", "/projects/%s/managedfolders/%s/usages" % (self.project_key, self.odb_id)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recipes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Recipe]:\n pass",
"def recipe(self):\n return self.__recipe",
"def used_in_recipes(self):\n Recipe = apps.get_model('recipes','Recipe')\n values = {}\n rqset = Recipe.objects.filter(co... | [
"0.6679568",
"0.6539516",
"0.65215296",
"0.62811166",
"0.60671085",
"0.6007505",
"0.6002195",
"0.59581256",
"0.5914185",
"0.58589774",
"0.5787091",
"0.5786681",
"0.5779459",
"0.5712589",
"0.55811965",
"0.5561164",
"0.55499417",
"0.5543476",
"0.55397946",
"0.5537948",
"0.55229... | 0.0 | -1 |
Get a handle to manage discussions on the managed folder. | def get_object_discussions(self):
return DSSObjectDiscussions(self.client, self.project_key, "MANAGED_FOLDER", self.odb_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_object_discussions(self):\n return DSSObjectDiscussions(self.client, self.project_key, \"RECIPE\", self.recipe_name)",
"def discussion(cls, user, discussion):\n pass",
"def discussion(cls, user, discussion):\r\n pass",
"def get_discussion(course):\r\n\r\n # the discussion_... | [
"0.54230756",
"0.5351559",
"0.53328663",
"0.5113142",
"0.5088055",
"0.5067406",
"0.48653966",
"0.4814929",
"0.47993654",
"0.47993654",
"0.4738103",
"0.47297895",
"0.4671899",
"0.4656283",
"0.4654367",
"0.46461034",
"0.46381405",
"0.46381405",
"0.46381405",
"0.46081758",
"0.45... | 0.61799204 | 0 |
Copy the data of this folder to another folder. | def copy_to(self, target, write_mode="OVERWRITE"):
dqr = {
"targetProjectKey" : target.project_key,
"targetFolderId": target.odb_id,
"writeMode" : write_mode
}
future_resp = self.client._perform_json("POST", "/projects/%s/managedfolders/%s/actions/copyTo" %... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_structure(self, other_directory):\n pass",
"def copydir(self):\n pass",
"def test_6b_copy_data_btw_folders(self):\n if (not GST.logged_in) or (not GST.data_testing_swift_mounted):\n raise unittest.SkipTest(\"Skipped for failed login or failed mounting container.\")\n ... | [
"0.7160782",
"0.7094813",
"0.6949282",
"0.6827731",
"0.6825672",
"0.66936094",
"0.6676309",
"0.65898836",
"0.6577749",
"0.64167154",
"0.64112085",
"0.64013064",
"0.6340882",
"0.63154685",
"0.631338",
"0.6297351",
"0.62772167",
"0.6256891",
"0.624406",
"0.62394696",
"0.6226650... | 0.0 | -1 |
Get the managef folder settings as a dict | def get_raw(self):
return self.settings | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_settings():\n settings = {}\n for setting in cfg.displayable_setting:\n settings[setting] = getattr(cfg, setting)\n return settings",
"def get_settings():\n settings = {}\n for setting in cfg.displayable_setting:\n settings[setting] = getattr(cfg, setting)\n return setting... | [
"0.7012647",
"0.7012647",
"0.69287425",
"0.67896175",
"0.6693999",
"0.66781723",
"0.66173553",
"0.6590877",
"0.65876055",
"0.6529215",
"0.64393365",
"0.6418583",
"0.64123183",
"0.6404648",
"0.63814056",
"0.63528734",
"0.6349637",
"0.63409376",
"0.63409376",
"0.63320076",
"0.6... | 0.5839828 | 55 |
Get the typespecific (S3/ filesystem/ HDFS/ ...) params as a dict. | def get_raw_params(self):
return self.settings["params"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameter_type_dict():\n return {'filter' : filters.filter_parameters,\n 'global_options' : global_options.global_options_parameters,\n 'input_device' : input_devices.input_device_parameters,\n 'input_stream' : input_streams.input_stream_parameters,\n 'output_devi... | [
"0.69671685",
"0.66325504",
"0.6175945",
"0.6123394",
"0.6078928",
"0.6034637",
"0.5959918",
"0.5933721",
"0.58757627",
"0.5868727",
"0.5856003",
"0.5853152",
"0.58497125",
"0.5848143",
"0.58415884",
"0.57942",
"0.57708806",
"0.5767855",
"0.57385707",
"0.573573",
"0.57297975"... | 0.5240058 | 82 |
Get the type of filesystem that the managed folder uses. | def type(self):
return self.settings["type"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_fs_type(self):\n\t\treturn call_sdk_function('PrlFsInfo_GetFsType', self.handle)",
"def get_type(self):\n return self.get_udev_property('ID_FS_TYPE')",
"def get_fs_type(mypath):\n\n root_type = ''\n for part in psutil.disk_partitions():\n if part.mountpoint == os.path.sep:\n ... | [
"0.76543826",
"0.7496883",
"0.7057454",
"0.67794114",
"0.676383",
"0.6749858",
"0.6724022",
"0.6706382",
"0.6657982",
"0.6648063",
"0.66008204",
"0.6565318",
"0.6494314",
"0.64438534",
"0.64036804",
"0.63732034",
"0.63719696",
"0.6364353",
"0.6363546",
"0.63634443",
"0.636344... | 0.0 | -1 |
Save the changes to the settings on the managed folder. | def save(self):
self.folder.client._perform_empty(
"PUT", "/projects/%s/managedfolders/%s" % (self.folder.project_key, self.folder.odb_id),
body=self.settings) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self):\n self.client._perform_empty(\"PUT\", \"/project-folders/%s/settings\" % (self.project_folder_id), body = self.settings)",
"def saveSettings(self):\n self.userFiles.applyData()\n self.userPersonal.applyData()",
"def save(self):\n return self.client._perform_empty(\"P... | [
"0.76129395",
"0.72382766",
"0.7231987",
"0.7208549",
"0.7072421",
"0.70549095",
"0.69852805",
"0.6924624",
"0.6886411",
"0.68704027",
"0.684195",
"0.6815835",
"0.6777165",
"0.67606515",
"0.6706621",
"0.6703455",
"0.66838694",
"0.66620696",
"0.66322386",
"0.6589611",
"0.65859... | 0.7415783 | 1 |
Make the managed folder nonpartitioned. | def remove_partitioning(self):
self.settings["partitioning"] = {"dimensions" : []} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createFolder(self):\n raise NotImplementedError",
"def mk_filesystem(self, folder_name):\n try:\n print \"%s FOLDER\" % folder_name\n c_m.mk_directory(self.main_path, self.domain_folder_name, folder_name)\n except WindowsError:\n print \"'%s' FOLDER ALREA... | [
"0.56322306",
"0.5550962",
"0.5330932",
"0.5274259",
"0.5269054",
"0.525035",
"0.5235328",
"0.5220792",
"0.520276",
"0.51945955",
"0.5191268",
"0.5187746",
"0.5164273",
"0.5148986",
"0.51358706",
"0.51303196",
"0.51252735",
"0.51183814",
"0.51160645",
"0.5096503",
"0.5077649"... | 0.5433089 | 2 |
Add a discrete partitioning dimension. | def add_discrete_partitioning_dimension(self, dim_name):
self.settings["partitioning"]["dimensions"].append({"name": dim_name, "type": "value"}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_partition(self):\n\t\treturn handle_to_object(call_sdk_function('PrlVmDevHd_AddPartition', self.handle))",
"def add_time_partitioning_dimension(self, dim_name, period=\"DAY\"):\n self.settings[\"partitioning\"][\"dimensions\"].append({\"name\": dim_name, \"type\": \"time\", \"params\":{\"period\":... | [
"0.5996331",
"0.59021497",
"0.5741625",
"0.563048",
"0.563048",
"0.55876046",
"0.5564135",
"0.55327845",
"0.54887104",
"0.5450582",
"0.5427648",
"0.54159683",
"0.5277757",
"0.5268855",
"0.5246866",
"0.5199661",
"0.513178",
"0.50552285",
"0.5037322",
"0.5004306",
"0.49957278",... | 0.8221045 | 0 |
Add a time partitioning dimension. | def add_time_partitioning_dimension(self, dim_name, period="DAY"):
self.settings["partitioning"]["dimensions"].append({"name": dim_name, "type": "time", "params":{"period": period}}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_discrete_partitioning_dimension(self, dim_name):\n self.settings[\"partitioning\"][\"dimensions\"].append({\"name\": dim_name, \"type\": \"value\"})",
"def add_timedim(data, date=\"1970-01-01\"):\n if isinstance(data, xr.DataArray):\n if \"time\" in data.dims:\n raise ValueErr... | [
"0.63332134",
"0.6205126",
"0.56875503",
"0.5681772",
"0.5438656",
"0.5215479",
"0.52103144",
"0.52042055",
"0.5199717",
"0.5189618",
"0.5118998",
"0.5118998",
"0.511268",
"0.5108713",
"0.50541395",
"0.5043726",
"0.50367475",
"0.5027835",
"0.5004906",
"0.49656916",
"0.4962021... | 0.8382509 | 0 |
Set the partitioning pattern of the folder. The pattern indicates which paths inside the folder belong to | def set_partitioning_file_pattern(self, pattern):
self.settings["partitioning"]["filePathPattern"] = pattern | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setSplitPattern(self, value):\n return self._set(splitPattern=value)",
"def setSplitPattern(self, value):\n return self._set(splitPattern=value)",
"def pattern(self, pattern):\n if pattern is None:\n raise ValueError(\"Invalid value for `pattern`, must not be `None`\") # no... | [
"0.6177902",
"0.6177902",
"0.5773725",
"0.57375664",
"0.5704174",
"0.558471",
"0.54004574",
"0.522178",
"0.522178",
"0.5215508",
"0.5176763",
"0.51129144",
"0.51109886",
"0.5100194",
"0.5096467",
"0.5093923",
"0.5066452",
"0.5036985",
"0.49579346",
"0.4954718",
"0.4954279",
... | 0.83221745 | 0 |
Change the managed folder connection and/or path. | def set_connection_and_path(self, connection, path):
if connection is not None:
if connection != self.settings["params"]["connection"]:
# get the actual connection type (and check that it exists)
connection_info = self.folder.client.get_connection(connection).get_info... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_path_service(self, new_path):\n self.__repo.set_path_repo(new_path)",
"def syncfolder():",
"def ChangeDir(self, path: str) -> None:\n ...",
"def set_basedir(self, host, path):",
"def set_local_path(self):\n return HERE",
"def chdir(self, path):\n if not path:\n ... | [
"0.62669915",
"0.59281373",
"0.58305687",
"0.5652025",
"0.5542018",
"0.552122",
"0.5518593",
"0.5472491",
"0.54537106",
"0.54523546",
"0.5446311",
"0.544534",
"0.5391472",
"0.53361374",
"0.5331498",
"0.53221756",
"0.53136843",
"0.52869874",
"0.52862704",
"0.5274655",
"0.52733... | 0.56019104 | 4 |
Get the predicted cost for each of the actions given the provided context. | def get_costs_per_action(self, context: np.ndarray) -> Dict[Action, Cost]:
costs_per_action = {}
for action in self._get_actions():
if self.categorize_actions:
action_one_hot = self._get_actions_one_hot(action)
x = np.append(action_one_hot, context)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(self, context: np.ndarray) -> np.ndarray:\n n_rounds_of_new_data = context.shape[0]\n ones_n_rounds_arr = np.ones(n_rounds_of_new_data, int)\n estimated_rewards_by_reg_model = np.zeros(\n (n_rounds_of_new_data, self.n_actions, self.len_list)\n )\n for actio... | [
"0.6950889",
"0.6194891",
"0.6161221",
"0.5940927",
"0.5893304",
"0.5862886",
"0.5833096",
"0.57626337",
"0.57062304",
"0.56541",
"0.5639965",
"0.5606558",
"0.5593295",
"0.5560387",
"0.5553072",
"0.55465263",
"0.5540782",
"0.55235296",
"0.55216396",
"0.54918563",
"0.54878634"... | 0.748326 | 0 |
Predict an action given a context. | def predict(
self,
context: np.ndarray,
epsilon: Prob = 0.05,
exploration_width: int = 1,
exploration_strategy: str = "smart",
) -> Tuple[Action, Prob]:
def _get_direction(action_change: Action) -> Optional[str]:
if action_change < 0:
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_fn(future_action, state):\n model = get_model()\n rewards = model((state, future_action))\n return {\"reward\": rewards}",
"def fit_predict(\n self,\n context: np.ndarray,\n action: np.ndarray,\n reward: np.ndarray,\n pscore: Optional[np.ndarray] = None,\n ... | [
"0.68578327",
"0.66418797",
"0.66278553",
"0.6600217",
"0.6558365",
"0.6513659",
"0.6338011",
"0.6164591",
"0.6121328",
"0.61089677",
"0.61044675",
"0.6058313",
"0.6042822",
"0.601579",
"0.59879005",
"0.59817743",
"0.5955881",
"0.5951787",
"0.5791318",
"0.5769036",
"0.5753873... | 0.6694794 | 1 |
Write a new training example in the logged data and retrain the regression model using the accumulated training data. | def learn(self, context: np.ndarray, action: Action, cost: Cost, prob: Prob):
if self.reg is None:
self._init_regressor(context)
self._log_example(context, action, cost, prob)
data = self.logged_data
probs = data[:, 0]
ips = 1 / probs
weights = ips * (np.linsp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def retrain_dl(self):\n if self.print_sequential:\n print(\"Opening files...\")\n data=self.open_files()\n if self.print_sequential:\n print(\"Generating training data and labels...\")\n train_data, label_data=self.transpose_load_concat(**data)\n if self.pri... | [
"0.6452068",
"0.64388776",
"0.6377261",
"0.6311379",
"0.62070644",
"0.61556226",
"0.6120951",
"0.60367054",
"0.6029556",
"0.60158366",
"0.5984059",
"0.59618765",
"0.59618765",
"0.59563255",
"0.5943222",
"0.5938815",
"0.59098",
"0.5908029",
"0.59024644",
"0.586966",
"0.5855478... | 0.0 | -1 |
Create a new parser for the nstl microlanguage. | def __init__(self, lexoptimize=True, lextab='_lextab',
yaccoptimize=True, yacctab='_yacctab', yaccdebug=False):
self.lexer = lex.NstlLexer()
self.lexer.build(optimize=lexoptimize, lextab=lextab)
self.tokens = self.lexer.tokens
self.parser = yacc.yacc(module=self, debug=yaccdebug,
optimize=yaccopti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_minilang_parser():\n gramm = Grammar.from_string(GRAMMAR)\n return parser_from_grammar(gramm, 'program')",
"def create_parser():\n pass",
"def buildParser( declaration = grammar ):\n return VRMLParser( declaration, \"vrmlFile\" )",
"def make_parser(language):\n parser = Parser()\n ... | [
"0.63348037",
"0.6227513",
"0.6044293",
"0.5954293",
"0.5915",
"0.5759258",
"0.557413",
"0.55248976",
"0.545088",
"0.5433322",
"0.5407149",
"0.53752893",
"0.5319015",
"0.53167206",
"0.5292922",
"0.52856356",
"0.5277806",
"0.52564335",
"0.5242387",
"0.5236219",
"0.52134985",
... | 0.5117509 | 29 |
This function accumulates tokens in a sequence or list. This is useful for all non terminals with the following pattern. | def accumulate(self, p, skip=0):
return [p[1]] if len(p) == 2 else p[1] + [p[2+skip]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_tokens(self, tokens):\n self.result.extend([d for d in tokens])",
"def add_tokens(self, tokens):\n if self.pad:\n tokens = [START_OF_SEQ] * self.order + tokens + [END_OF_SEQ]\n\n for i in range(len(tokens) - self.order):\n current_state = tuple(tokens[i:i + self... | [
"0.6333529",
"0.6308854",
"0.6282478",
"0.6102212",
"0.60056746",
"0.5859196",
"0.5844713",
"0.5804765",
"0.5756415",
"0.5645834",
"0.5570865",
"0.5556612",
"0.55548114",
"0.55465573",
"0.5507698",
"0.54293084",
"0.54293084",
"0.54293084",
"0.5410935",
"0.536951",
"0.5361517"... | 0.50384396 | 43 |
Tests if dict gets properly converted to NaElements. | def test_translate_struct_dict_unique_key(self):
root = netapp_api.NaElement('root')
child = {'e1': 'v1', 'e2': 'v2', 'e3': 'v3'}
root.translate_struct(child)
self.assertEqual(len(root.get_children()), 3)
self.assertEqual(root.get_child_content('e1'), 'v1')
self.assertEqu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_translate_struct_dict_nonunique_key(self):\n root = netapp_api.NaElement('root')\n child = [{'e1': 'v1', 'e2': 'v2'}, {'e1': 'v3'}]\n root.translate_struct(child)\n self.assertEqual(len(root.get_children()), 3)\n children = root.get_children()\n for c in children:... | [
"0.623892",
"0.6218801",
"0.5750569",
"0.5704162",
"0.5685371",
"0.56784874",
"0.5639726",
"0.5624354",
"0.5584869",
"0.5556947",
"0.5548787",
"0.553519",
"0.54804677",
"0.54760355",
"0.5473484",
"0.54730076",
"0.5444948",
"0.5435633",
"0.5421339",
"0.5413401",
"0.53997535",
... | 0.5133013 | 44 |
Tests if list/dict gets properly converted to NaElements. | def test_translate_struct_dict_nonunique_key(self):
root = netapp_api.NaElement('root')
child = [{'e1': 'v1', 'e2': 'v2'}, {'e1': 'v3'}]
root.translate_struct(child)
self.assertEqual(len(root.get_children()), 3)
children = root.get_children()
for c in children:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _list_like(self, value):\n return (not hasattr(value, \"strip\") and\n (hasattr(value, \"__getitem__\") or\n hasattr(value, \"__iter__\")))\n # return is_sequence(value) # use from pandas.core.common import is_sequence",
"def isnondet(r):\n return isinstance(r, list... | [
"0.60055685",
"0.58362675",
"0.55918443",
"0.55426055",
"0.54347575",
"0.53278166",
"0.53273875",
"0.53242916",
"0.53021944",
"0.529288",
"0.52613616",
"0.5240428",
"0.52191174",
"0.52089655",
"0.51931584",
"0.51931584",
"0.51650697",
"0.5162607",
"0.5159848",
"0.5155062",
"0... | 0.50057566 | 40 |
Tests if list gets properly converted to NaElements. | def test_translate_struct_list(self):
root = netapp_api.NaElement('root')
child = ['e1', 'e2']
root.translate_struct(child)
self.assertEqual(len(root.get_children()), 2)
self.assertIsNone(root.get_child_content('e1'))
self.assertIsNone(root.get_child_content('e2')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nonull(inputlist):\n return clean(inputlist, isnull, True)",
"def _list_like(self, value):\n return (not hasattr(value, \"strip\") and\n (hasattr(value, \"__getitem__\") or\n hasattr(value, \"__iter__\")))\n # return is_sequence(value) # use from pandas.core.common impo... | [
"0.6358113",
"0.60206056",
"0.5883476",
"0.58625174",
"0.58625174",
"0.5800553",
"0.57959276",
"0.5734356",
"0.5712273",
"0.5690614",
"0.563991",
"0.5604465",
"0.55697477",
"0.55463994",
"0.55229896",
"0.54853135",
"0.54730946",
"0.54585755",
"0.54563",
"0.5433329",
"0.541858... | 0.5204703 | 41 |
Tests if tuple gets properly converted to NaElements. | def test_translate_struct_tuple(self):
root = netapp_api.NaElement('root')
child = ('e1', 'e2')
root.translate_struct(child)
self.assertEqual(len(root.get_children()), 2)
self.assertIsNone(root.get_child_content('e1'))
self.assertIsNone(root.get_child_content('e2')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_tuples():\n\n @type_checked\n def _run_test(something:(str, int, bool)):\n assert isinstance(something[0], str)\n assert isinstance(something[1], int)\n assert isinstance(something[2], bool)\n\n _run_test(something=(None, \"12\", 1))",
"def _is_positive_int_tuple(item):\n ... | [
"0.6304577",
"0.62479115",
"0.6131925",
"0.6101338",
"0.60980195",
"0.59517014",
"0.59246325",
"0.587698",
"0.5869051",
"0.58413464",
"0.578344",
"0.5762512",
"0.5754428",
"0.5734058",
"0.569836",
"0.5663851",
"0.56290215",
"0.56237906",
"0.5618846",
"0.56172585",
"0.56099147... | 0.600591 | 5 |
Tests if invalid data structure raises exception. | def test_translate_invalid_struct(self):
root = netapp_api.NaElement('root')
child = 'random child element'
self.assertRaises(ValueError, root.translate_struct, child) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_with_invalid_input(self):\n for dataset_type in ['ruler', 'pencil', 'cheese']:\n with self.assertRaises(ValueError) as exc:\n check_dataset_type(dataset_type)\n self.assertEqual(\"Dataset type not 'regular' or 'raw' is %s\" % dataset_type,\n ... | [
"0.69549423",
"0.6800185",
"0.67051315",
"0.66807365",
"0.6600718",
"0.65539765",
"0.6551458",
"0.65252626",
"0.6507101",
"0.65033543",
"0.650226",
"0.6499803",
"0.6486112",
"0.6415248",
"0.6384",
"0.6347746",
"0.6316549",
"0.6275802",
"0.6264271",
"0.62269646",
"0.62124825",... | 0.59542996 | 71 |
Tests str, int, float get converted to NaElement. | def test_setter_builtin_types(self):
root = netapp_api.NaElement('root')
root['e1'] = 'v1'
root['e2'] = 1
root['e3'] = 2.0
root['e4'] = 8l
self.assertEqual(len(root.get_children()), 4)
self.assertEqual(root.get_child_content('e1'), 'v1')
self.assertEqual(r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_string_or_number():\n assert is_string_or_number(None) is None\n assert is_string_or_number(1) is None\n assert is_string_or_number(1.1) is None\n assert is_string_or_number('1.1') is None\n assert is_string_or_number([])",
"def ele2nb(element):\n if isinstance(element, str):\n return flo... | [
"0.60229194",
"0.5978814",
"0.58429635",
"0.57734835",
"0.5629192",
"0.5614145",
"0.55944276",
"0.5573304",
"0.5557818",
"0.55009377",
"0.55009377",
"0.54978293",
"0.5463476",
"0.54627395",
"0.5459325",
"0.54479104",
"0.5445536",
"0.541506",
"0.5411362",
"0.5389325",
"0.53625... | 0.51746476 | 40 |
Tests na_element gets appended as child. | def test_setter_na_element(self):
root = netapp_api.NaElement('root')
root['e1'] = netapp_api.NaElement('nested')
self.assertEqual(len(root.get_children()), 1)
e1 = root.get_child_by_name('e1')
self.assertIsInstance(e1, netapp_api.NaElement)
self.assertIsInstance(e1.get_c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_setter_na_element(self):\n root = netapp_api.NaElement('root')\n root['e1'] = netapp_api.NaElement('nested')\n self.assertEqual(1, len(root.get_children()))\n e1 = root.get_child_by_name('e1')\n self.assertIsInstance(e1, netapp_api.NaElement)\n self.assertIsInstan... | [
"0.67987376",
"0.63512254",
"0.6176601",
"0.6144186",
"0.60023457",
"0.5950926",
"0.59426093",
"0.5894436",
"0.5872535",
"0.58458453",
"0.58127284",
"0.58080167",
"0.5802401",
"0.5756666",
"0.57045996",
"0.5687755",
"0.5677121",
"0.5653698",
"0.5644743",
"0.56394815",
"0.5633... | 0.67678875 | 1 |
Tests dict is appended as child to root. | def test_setter_child_dict(self):
root = netapp_api.NaElement('root')
root['d'] = {'e1': 'v1', 'e2': 'v2'}
e1 = root.get_child_by_name('d')
self.assertIsInstance(e1, netapp_api.NaElement)
sub_ch = e1.get_children()
self.assertEqual(len(sub_ch), 2)
for c in sub_ch:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_setter_child_dict(self):\n root = netapp_api.NaElement('root')\n root['d'] = {'e1': 'v1', 'e2': 'v2'}\n e1 = root.get_child_by_name('d')\n self.assertIsInstance(e1, netapp_api.NaElement)\n sub_ch = e1.get_children()\n self.assertEqual(2, len(sub_ch))\n for ... | [
"0.692969",
"0.62596947",
"0.6051907",
"0.6033617",
"0.6024387",
"0.6024387",
"0.6024387",
"0.6024387",
"0.6024387",
"0.5993601",
"0.5961713",
"0.5925168",
"0.5859408",
"0.58378726",
"0.58262926",
"0.58179736",
"0.58094585",
"0.5797076",
"0.575363",
"0.57313854",
"0.57068044"... | 0.6954826 | 0 |
Tests list/tuple are appended as child to root. | def test_setter_child_list_tuple(self):
root = netapp_api.NaElement('root')
root['l'] = ['l1', 'l2']
root['t'] = ('t1', 't2')
l = root.get_child_by_name('l')
self.assertIsInstance(l, netapp_api.NaElement)
t = root.get_child_by_name('t')
self.assertIsInstance(t, ne... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_setter_child_list_tuple(self):\n root = netapp_api.NaElement('root')\n root['l'] = ['l1', 'l2']\n root['t'] = ('t1', 't2')\n l_element = root.get_child_by_name('l')\n self.assertIsInstance(l_element, netapp_api.NaElement)\n t = root.get_child_by_name('t')\n ... | [
"0.70010734",
"0.65542954",
"0.63257587",
"0.6279599",
"0.62566125",
"0.6221631",
"0.6212001",
"0.6183939",
"0.6152893",
"0.61014795",
"0.60733724",
"0.60378975",
"0.59894735",
"0.5981031",
"0.5951861",
"0.5948126",
"0.5942114",
"0.59398097",
"0.5915376",
"0.5901368",
"0.5882... | 0.70634353 | 0 |
Tests key with None value. | def test_setter_no_value(self):
root = netapp_api.NaElement('root')
root['k'] = None
self.assertIsNone(root.get_child_content('k')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def func3(key):\n value = my_test_dict.get(key)\n if value is None:\n return False\n else:\n return True",
"def test_key_no_data(self):\n key = Key({})\n\n assert key.warning is None\n assert key.in_car is None",
"def compare_with_none():\n value = {};\n if val... | [
"0.7376511",
"0.7210644",
"0.7156653",
"0.7106254",
"0.7106254",
"0.70963234",
"0.70963234",
"0.68859714",
"0.67631936",
"0.67631936",
"0.6726326",
"0.6660834",
"0.65424436",
"0.65263426",
"0.6464883",
"0.6434181",
"0.6414277",
"0.63221276",
"0.6299831",
"0.6287927",
"0.62784... | 0.5739828 | 78 |
Tests invalid value raises exception. | def test_setter_invalid_value(self):
root = netapp_api.NaElement('root')
try:
root['k'] = netapp_api.NaServer('localhost')
except Exception as e:
if not isinstance(e, TypeError):
self.fail(_('Error not a TypeError.')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_value_error(self):\n self._error_test(ValueError)",
"def test_task_with_one_int_validation_parameter_validate_exceptions(number, expected_value):\r\n\r\n with pytest.raises(expected_value):\r\n algo.TaskWithOneIntValidationParameter.validate_data(number)",
"def test_bad_values(self):\... | [
"0.8108551",
"0.75570345",
"0.7404357",
"0.73849297",
"0.72685856",
"0.72426933",
"0.72107214",
"0.72030556",
"0.715305",
"0.7153031",
"0.71462476",
"0.7118535",
"0.7113288",
"0.71109194",
"0.7099144",
"0.7038161",
"0.7006992",
"0.69981664",
"0.6976003",
"0.6958428",
"0.69519... | 0.0 | -1 |
Tests invalid value raises exception. | def test_setter_invalid_key(self):
root = netapp_api.NaElement('root')
try:
root[None] = 'value'
except Exception as e:
if not isinstance(e, KeyError):
self.fail(_('Error not a KeyError.')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_value_error(self):\n self._error_test(ValueError)",
"def test_task_with_one_int_validation_parameter_validate_exceptions(number, expected_value):\r\n\r\n with pytest.raises(expected_value):\r\n algo.TaskWithOneIntValidationParameter.validate_data(number)",
"def test_bad_values(self):\... | [
"0.8108551",
"0.75570345",
"0.7404357",
"0.73849297",
"0.72685856",
"0.72426933",
"0.72107214",
"0.72030556",
"0.715305",
"0.7153031",
"0.71462476",
"0.7118535",
"0.7113288",
"0.71109194",
"0.7099144",
"0.7038161",
"0.7006992",
"0.69981664",
"0.6976003",
"0.6958428",
"0.69519... | 0.0 | -1 |
Get sentiment analysis immediately on document save | def get_sentiment_analysis(sender, instance, **kwargs):
text_analysis = TextAnalysis(instance.text)
# Prevent sentiment_analysis API call every time the document is saved
if instance.sentiment_analysis is None:
instance.get_sentiment_analysis() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text_analytics(self):\n\n headers = {\n # Request headers\n 'Content-Type': 'application/json',\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\n }\n \n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/senti... | [
"0.66183805",
"0.63964885",
"0.63705045",
"0.63385326",
"0.63300335",
"0.6323211",
"0.62792253",
"0.61452967",
"0.61425865",
"0.6107899",
"0.608918",
"0.60816747",
"0.60478175",
"0.6037236",
"0.59819293",
"0.59520787",
"0.5950468",
"0.5949469",
"0.5940747",
"0.5906748",
"0.59... | 0.69361526 | 0 |
This class takes care of putting the text preprocessing, label encoding and model into a classification pipeline. Labels are onehot encoded with sklearn's LabelBinarizer, text is tokenized with the TextFormatting class in preprocessing, and the model is the TextClassifier in model. | def __init__(self,
sequence_length: int,
embeddings_dim: int,
embeddings_path: str = None):
self.label_encoder = LabelBinarizer()
self.text_formatter = TextFormatting(max_len=sequence_length)
self.sequence_length = sequence_length
self.v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classify(text):\n # TODO Wonder if there's a better way of doing this so the model persists across fucn calls. Will see once I get\n # Heroku running\n\n sentences = sent_tokenize(text)\n clean_sentences = list(map(clean_text, sentences))\n word_tokenizer = BertTokenizerFast.from_pretrained('be... | [
"0.68297",
"0.65293926",
"0.65250844",
"0.6384357",
"0.63417363",
"0.6297352",
"0.62855387",
"0.6263438",
"0.62505645",
"0.6246545",
"0.62384707",
"0.6237421",
"0.6180101",
"0.61683106",
"0.61633414",
"0.615634",
"0.61422986",
"0.6137567",
"0.6129089",
"0.6121658",
"0.6110423... | 0.0 | -1 |
Fits the model to the training data x and its associated labels y. The model will be recorded in self.model. | def fit(self, x: pd.Series, y: pd.Series, **fit_kwargs):
x = self.text_formatter.fit_transform(x)
y_one_hot = self.label_encoder.fit_transform(y)
if y_one_hot.shape[1] == 1:
y_one_hot = np.hstack((y_one_hot, 1 - y_one_hot))
self._fit(x, y_one_hot, **fit_kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, X, y):\n self.model_x = X\n self.model_y = y",
"def train(self, X, y):\n self.model.fit(X, y)",
"def train(self):\n\t\tself.model.fit(self.training_data, self.training_labels)",
"def training(self):\n self.model.fit(self.train_x, self.train_y)",
"def train(self, X_... | [
"0.7848193",
"0.78104174",
"0.77299017",
"0.7567453",
"0.75329787",
"0.7435346",
"0.7343392",
"0.7276514",
"0.7276514",
"0.72682047",
"0.71949565",
"0.71926683",
"0.7164424",
"0.71145386",
"0.7070291",
"0.7055931",
"0.7043422",
"0.7040537",
"0.69801044",
"0.69582874",
"0.6927... | 0.0 | -1 |
Performs cross validation and returns the scores. | def cv(self, x: pd.Series, y: pd.Series, n_splits: int, refit: bool = True, **fit_kwargs) -> List[list]:
x = self.text_formatter.fit_transform(x)
y_one_hot = self.label_encoder.fit_transform(y)
if y_one_hot.shape[1] == 1:
y_one_hot = np.hstack((y_one_hot, 1 - y_one_hot))
skf ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cross_validate_model(self, X_train, y_train):\n\n\t\t# Build a stratified k-fold cross-validator object\n\t\tskf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n\n\t\t'''\n\t\tEvaluate the score by cross-validation\n\t\tThis fits the classification model on the training data, according to the cr... | [
"0.75626475",
"0.7517389",
"0.74758613",
"0.73620546",
"0.7360652",
"0.73598105",
"0.7357008",
"0.7326522",
"0.7309351",
"0.7306647",
"0.7275394",
"0.72500217",
"0.72362286",
"0.71839345",
"0.7173292",
"0.71140176",
"0.7035299",
"0.7027727",
"0.7023638",
"0.7022257",
"0.70222... | 0.0 | -1 |
Generates predictions using the trained model and preprocessing. | def predict(self, x: Union[List[str], pd.Series]) -> np.array:
predictions = self._predict(x)
return self.label_encoder.inverse_transform(predictions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_predictions(fitted_model_filename):\n click.echo(\"Mode: predicting probabilities.\\n\")\n defaults = get_defaults()\n\n fitted_model_filename = add_extension(fitted_model_filename)\n fitted_model_path = os.path.join(defaults.OUTPUT.FITTED_MODELS_PATH, fitted_model_filename)\n new_options = ... | [
"0.7347879",
"0.70187277",
"0.70089066",
"0.69615614",
"0.69180834",
"0.6915808",
"0.6898056",
"0.6853602",
"0.6763513",
"0.6751476",
"0.6718363",
"0.6702563",
"0.6698698",
"0.66934955",
"0.66716003",
"0.6671341",
"0.66694397",
"0.66694397",
"0.6665388",
"0.66317606",
"0.6616... | 0.0 | -1 |
Returns the raw prediction (all probabilities for all classes) | def predict_proba(self, x):
return self._predict(x) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predictions(self):\n return self._pred",
"def get_prediction(self):\n return self.prediction",
"def get_classification_predictions(self):\n predictions = []\n for i, test_batch in enumerate(tqdm.tqdm(self.loader)):\n if self.tta_fn is not None:\n pred_o... | [
"0.7318895",
"0.72432023",
"0.7131954",
"0.70452535",
"0.7043757",
"0.7003312",
"0.69766957",
"0.6888829",
"0.6861435",
"0.6841351",
"0.6831913",
"0.68016833",
"0.6754687",
"0.6747136",
"0.67463976",
"0.6743526",
"0.6729051",
"0.6728815",
"0.6722753",
"0.67097694",
"0.6700957... | 0.0 | -1 |
This function is used in the property self.embeddings. | def set_embeddings(self): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_movie_embedding(self):\n raise NotImplementedError(\"has to be overwritten\")",
"def add_embedding(self):\n ### YOUR CODE HERE (~4-6 lines)\n embeddingTensor = tf.Variable(self.pretrained_embeddings)\n embeddings = tf.nn.embedding_lookup(embeddingTensor, self.input_placeholder... | [
"0.74282503",
"0.702405",
"0.68440545",
"0.6790293",
"0.6766997",
"0.6700022",
"0.6659412",
"0.6578466",
"0.6472033",
"0.6452144",
"0.6434908",
"0.6428523",
"0.6398044",
"0.6394544",
"0.6383916",
"0.6372758",
"0.63573927",
"0.63538617",
"0.63432187",
"0.633421",
"0.6262197",
... | 0.8120444 | 0 |
For each string, output 1 if the DFA accepts it, 0 otherwise. The input is guaranteed to be a DFA. | def task_4(parser):
dfa = parser.parse_fa()
test_strings = parser.parse_test_strings()
# calculate and print acceptance for each string
for string in test_strings:
if follow_dfa(dfa["graph"][dfa["start"]], string):
print("1")
else:
print("0")
print("end") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, s):\n state = self._initial\n try:\n for sym in s:\n state = self._trans_matrix[state][self._syms_to_indices[sym]]\n except KeyError:\n raise NotInAlphabetError(sym) from None\n return state in self._accepting",
"def isogram():\n... | [
"0.6033226",
"0.5742265",
"0.5733216",
"0.56888366",
"0.56711626",
"0.56197566",
"0.5588654",
"0.5418547",
"0.5407468",
"0.5398522",
"0.5388671",
"0.5385906",
"0.5385906",
"0.5385906",
"0.5385906",
"0.53602785",
"0.53272057",
"0.52750105",
"0.5264539",
"0.5249218",
"0.5243929... | 0.63068765 | 0 |
Recursively follows states until string is empty. Returns whether state is terminal. | def follow_dfa(state, string):
if string == "":
return state["final"]
# get first edge using symbol at beginning of string
# next is a cool function ive just learned i hope this counts as readable code 🥺👉👈
next_state = next(
s["node"] for s in state["edges"]
if s["symbol"] ==... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_terminal(self, state):\n return len(self.get_possible_actions(state)) == 0",
"def is_terminal(self, state):\n x, y = self.__state_to_xy(state)\n if MAP[y][x] in ['G', 'H']:\n return True\n return False",
"def is_terminal(state):\n\n # Horizontal check\n for i... | [
"0.6522898",
"0.6326616",
"0.6221845",
"0.6193005",
"0.6183206",
"0.61662275",
"0.61294013",
"0.6114354",
"0.59763944",
"0.5969359",
"0.59522396",
"0.59356666",
"0.5826155",
"0.57757306",
"0.5694262",
"0.56490445",
"0.5636367",
"0.55809706",
"0.55656576",
"0.55133486",
"0.550... | 0.55716324 | 18 |
access remote with under wechat'api's interface just a simple wrapper on `get_remote` raise error on response error | def _access_wxapi_or_raise(self, *args, **kwargs):
r = json.loads(get_remote(*args, **kwargs))
if "errcode" in r:
raise Exception("errcode: " + str(r["errcode"]) + ", errmsg: " + r["errmsg"])
return r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote():\n pass",
"def remote(self, *args, **kwargs):\n return self.api.remote(*args, **kwargs)",
"def getRemoteHost():",
"def remote(self, *arguments, **kwargs):\n return self.get_output('remote', *arguments, **kwargs)",
"def do_remote(self, *args):\n return self.do_scpi(':com... | [
"0.67932016",
"0.6746072",
"0.6663715",
"0.66510284",
"0.6398095",
"0.6062869",
"0.59665376",
"0.5951629",
"0.57943213",
"0.5788961",
"0.5787939",
"0.57730424",
"0.5729481",
"0.5727869",
"0.5727869",
"0.5682217",
"0.56431633",
"0.56091577",
"0.5571601",
"0.55352926",
"0.55278... | 0.62832904 | 5 |
get access token from wxapi this is the second step to login with wechat after the client get the code | def get_access_token(self, code):
url = get_config("login.wechat.access_token_url") % code
r = self._access_wxapi_or_raise(url)
return (r["access_token"], r["openid"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_token(self, code):\n\n # live need post a form to get token\n headers = {'Content-type': 'application/x-www-form-urlencoded'}\n data = {\n 'client_id': get_config('login.live.client_id'),\n 'client_secret': get_config('login.live.client_secret'),\n 'red... | [
"0.6793707",
"0.67241335",
"0.6619643",
"0.6482753",
"0.64813423",
"0.6452798",
"0.6423836",
"0.64193034",
"0.64193034",
"0.63922495",
"0.63754576",
"0.6291259",
"0.6257184",
"0.62232846",
"0.6198705",
"0.6198705",
"0.6197053",
"0.61913085",
"0.6176826",
"0.61711794",
"0.6151... | 0.7249641 | 0 |
get user info from wxapi this is the final step to login with wechat | def get_user_info(self, access_token, openid):
url = get_config("login.wechat.user_info_url") % (access_token, openid)
return self._access_wxapi_or_raise(url) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_login():\n \n data = user_obj.user_login()\n return data",
"def login(self):",
"def login():",
"def login():",
"def log_in(self, ctx: Context):\n email = json.loads(ctx.users)['username']\n password = json.loads(ctx.users)['password']\n InputFunctions.send_keys_to_ele... | [
"0.6570302",
"0.6333091",
"0.62763906",
"0.62763906",
"0.6231804",
"0.6212389",
"0.620589",
"0.6194151",
"0.612593",
"0.6112595",
"0.61102265",
"0.6109876",
"0.60988086",
"0.6087747",
"0.6087747",
"0.606966",
"0.6067771",
"0.6058907",
"0.60572743",
"0.6048634",
"0.60352176",
... | 0.65810204 | 0 |
Get qq access token | def get_token(self, code, redirect_uri):
token_resp = get_remote(get_config("login.qq.access_token_url") % (redirect_uri, code))
if token_resp.find('callback') == 0:
error = json.loads(token_resp[10:-4])
raise Exception(error)
query = qs_dict(token_resp)
return q... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_access_token(self, request) -> str or Exception:\n pass",
"def access_token(self):\n return self.access_token_str",
"def getAccessToken(self):\r\n\r\n #lets see if we have an oauth code\r\n if self.oauthToken is None:\r\n self.oauthToken = self.createAccessToken\r\n\r... | [
"0.7010284",
"0.6989711",
"0.6904486",
"0.68345237",
"0.6801059",
"0.67715067",
"0.6744162",
"0.67331374",
"0.67299163",
"0.66792107",
"0.6677348",
"0.66704553",
"0.66563576",
"0.6632723",
"0.66103107",
"0.66000867",
"0.65999943",
"0.65748245",
"0.6555147",
"0.6550575",
"0.65... | 0.7126429 | 0 |
Get qq open id | def get_info(self, token):
openid_resp = get_remote(get_config("login.qq.openid_url") + token)
self.log.debug("get access_token from qq:" + token)
info = json.loads(openid_resp[10:-4])
if info.get("error") is not None:
raise Exception(info)
return info | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qid(self) -> str:\n return self._itempage.title()",
"def get_stream_id(self) -> str:",
"def __str__(self):\n return self.qseqid",
"def get_id(self): # real signature unknown; restored from __doc__\n return \"\"",
"def find_issue_id(self):",
"def getID():",
"def reqid(self) -> s... | [
"0.619482",
"0.61881024",
"0.59014285",
"0.5895853",
"0.5848636",
"0.5827127",
"0.57593566",
"0.57593566",
"0.567928",
"0.56763285",
"0.5628064",
"0.55315685",
"0.5506051",
"0.55008554",
"0.54917115",
"0.5425531",
"0.5415797",
"0.5415797",
"0.5415797",
"0.5415797",
"0.5415797... | 0.0 | -1 |
Get qq user info | def get_user_info(self, token, openid, client_id):
url = get_config("login.qq.user_info_url") % (token, client_id, openid)
user_info_resp = get_remote(url)
user_info = convert(json.loads(user_info_resp))
if user_info.get("ret") != 0:
raise Exception(user_info)
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\n response = self.query('user_info')\n return response",
"def get_user_info(self) -> str:\n return self._searcher.get_user_info()",
"def user_info(self):\n return self.auth.get_user_by_session()",
"def user_info(self):\r\n param = {}\r\n param['appid'] =... | [
"0.7060283",
"0.6837285",
"0.6771517",
"0.67457294",
"0.66713727",
"0.6424139",
"0.6392767",
"0.6378538",
"0.63607556",
"0.63139594",
"0.6248579",
"0.6244239",
"0.62231886",
"0.6194135",
"0.6159332",
"0.615596",
"0.61555964",
"0.6150874",
"0.6143393",
"0.61389005",
"0.6111682... | 0.6423132 | 6 |
Get github access token | def get_token(self, code):
token_url = get_config('login.github.access_token_url')
data_to_post = {
"client_id": get_config("login.github.client_id"),
"client_secret": get_config("login.github.client_secret"),
"code": str(code)
}
headers = {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_github_credendial(cls) -> 'ApiCredential':\n return cls.select_token_for_api(GITHUB_API_NAME)",
"def get_github_credentials():\n\n p = subprocess.Popen(\"git config github.accesstoken\",\n shell=True,\n stdout=subprocess.PIPE,\n ... | [
"0.7889109",
"0.7756136",
"0.77456725",
"0.767006",
"0.7652787",
"0.74576634",
"0.7299358",
"0.7048512",
"0.7019856",
"0.6951362",
"0.6916727",
"0.68040997",
"0.67905265",
"0.67753285",
"0.67421544",
"0.6734485",
"0.6664523",
"0.6646364",
"0.6640968",
"0.66376317",
"0.6619797... | 0.71718204 | 7 |
Get user primary email | def get_emails(self, token):
user_email_url = get_config('login.github.emails_info_url')
headers = {
"Authorization": "token %s" % token
}
email_info_resp = get_remote(user_email_url, headers)
email_list = json.loads(email_info_resp)
return email_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def email(self):\n # Look for a primary address\n useremail = UserEmail.query.filter_by(user_id=self.id, primary=True).first()\n if useremail:\n return useremail\n # No primary? Maybe there's one that's not set as primary?\n useremail = UserEmail.query.filter_by(user_i... | [
"0.80477",
"0.80093074",
"0.7920181",
"0.7907315",
"0.77828515",
"0.7753013",
"0.77459264",
"0.7690467",
"0.7659735",
"0.75809807",
"0.7466649",
"0.7466649",
"0.7378308",
"0.7369187",
"0.7355905",
"0.73503345",
"0.7347515",
"0.73376715",
"0.7324323",
"0.72827196",
"0.72159886... | 0.0 | -1 |
Get qq user info | def get_user_info(self, token):
user_info_url = get_config('login.github.user_info_url')
headers = {
"Authorization": "token %s" % token,
"Accept": "application/json"
}
user_info_resp = get_remote(user_info_url, headers)
user_info = json.loads(user_info_r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\n response = self.query('user_info')\n return response",
"def get_user_info(self) -> str:\n return self._searcher.get_user_info()",
"def user_info(self):\n return self.auth.get_user_by_session()",
"def user_info(self):\r\n param = {}\r\n param['appid'] =... | [
"0.7060283",
"0.6837285",
"0.6771517",
"0.67457294",
"0.66713727",
"0.6424139",
"0.6423132",
"0.6392767",
"0.6378538",
"0.63607556",
"0.63139594",
"0.6248579",
"0.6244239",
"0.62231886",
"0.6194135",
"0.6159332",
"0.615596",
"0.61555964",
"0.6150874",
"0.6143393",
"0.61389005... | 0.0 | -1 |
Get weibo access token | def get_token(self, code, redirect_uri):
token_resp = post_to_remote(get_config('login.weibo.access_token_url') % (redirect_uri, code), {})
if token_resp.get("error") is not None:
raise Exception(token_resp)
return token_resp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_token(request):\n capability = TwilioCapability(\n settings.TWILIO_ACCOUNT_SID,\n settings.TWILIO_AUTH_TOKEN)\n \"\"\"Allow our users to make outgoing calls with Twilio Client\"\"\"\n capability.allow_client_outgoing(settings.TWIML_APPLICATION_SID)\n\n \"\"\"Allow our users to acc... | [
"0.70288146",
"0.7001885",
"0.6894617",
"0.6847831",
"0.6780489",
"0.67765784",
"0.6754007",
"0.6753824",
"0.6739081",
"0.6731335",
"0.67164814",
"0.66781205",
"0.66739684",
"0.6635992",
"0.6626884",
"0.6626884",
"0.6622913",
"0.6604508",
"0.65591806",
"0.6532953",
"0.6530821... | 0.6161044 | 77 |
Get weibo user info | def get_user_info(self, token, uid):
# https://api.weibo.com/2/users/show.json?access_token=2.005RDjXC0rYD8d39ca83156aLZWgZE&uid=1404376560
user_info_resp = get_remote(get_config('login.weibo.user_info_url') + token + "&uid=" + uid)
user_info = json.loads(user_info_resp)
if user_info.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\r\n param = {}\r\n param['appid'] = self.apiKey\r\n param['nonce'] = int(time.time()*1000)\r\n param['timestamp'] = int(time.time())\r\n return self.__signed_GET('/api/v1/users/me', param, self.timeout)",
"def getBasicInfo(self):\n homepage_url = 'ht... | [
"0.71305436",
"0.7084169",
"0.7075696",
"0.6976193",
"0.6964103",
"0.6913651",
"0.6893832",
"0.68115276",
"0.68086296",
"0.67903674",
"0.6736626",
"0.67143106",
"0.6713513",
"0.66546255",
"0.664701",
"0.66461414",
"0.66090995",
"0.66029125",
"0.65716374",
"0.65706587",
"0.654... | 0.6753262 | 10 |
Get weibo user info | def get_email(self, token, uid):
email_info_resp = get_remote(get_config('login.weibo.email_info_url') + token)
email_info_resp_json = json.loads(email_info_resp)
if email_info_resp_json.get("error") is not None:
raise Exception(email_info_resp_json)
return email_info_resp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\r\n param = {}\r\n param['appid'] = self.apiKey\r\n param['nonce'] = int(time.time()*1000)\r\n param['timestamp'] = int(time.time())\r\n return self.__signed_GET('/api/v1/users/me', param, self.timeout)",
"def getBasicInfo(self):\n homepage_url = 'ht... | [
"0.71305436",
"0.7084169",
"0.7075696",
"0.6976193",
"0.6964103",
"0.6913651",
"0.6893832",
"0.68115276",
"0.68086296",
"0.67903674",
"0.6753262",
"0.6736626",
"0.67143106",
"0.6713513",
"0.66546255",
"0.664701",
"0.66461414",
"0.66090995",
"0.66029125",
"0.65716374",
"0.6570... | 0.0 | -1 |
Get live access token | def get_token(self, code):
# live need post a form to get token
headers = {'Content-type': 'application/x-www-form-urlencoded'}
data = {
'client_id': get_config('login.live.client_id'),
'client_secret': get_config('login.live.client_secret'),
'redirect_uri': ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_token(self):\n if self._access_token is None or self._is_expired():\n self._refresh_token()\n return self._access_token",
"def access_token(self):\n if self.has_expired():\n self.update()\n\n return self.token['access_token']",
"def getAccessToken(self... | [
"0.78363293",
"0.7810096",
"0.758454",
"0.7580243",
"0.7556684",
"0.7460324",
"0.7452582",
"0.744574",
"0.73868954",
"0.736419",
"0.7318679",
"0.73036116",
"0.72809124",
"0.72522765",
"0.7224583",
"0.72144043",
"0.72129726",
"0.7202701",
"0.7202701",
"0.7200073",
"0.7200073",... | 0.7708082 | 2 |
Get live user info | def get_user_info(self, token):
user_info_resp = get_remote(get_config('login.live.user_info_url') + token)
user_info = json.loads(user_info_resp)
if user_info.get("error") is not None:
raise Exception(user_info)
return user_info | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\r\n param = {}\r\n param['appid'] = self.apiKey\r\n param['nonce'] = int(time.time()*1000)\r\n param['timestamp'] = int(time.time())\r\n return self.__signed_GET('/api/v1/users/me', param, self.timeout)",
"def user_info(self):\n response = self.query... | [
"0.7842169",
"0.7835051",
"0.76801395",
"0.7642541",
"0.739176",
"0.7339232",
"0.72461045",
"0.7243062",
"0.72167087",
"0.7173714",
"0.7156141",
"0.7093393",
"0.7086855",
"0.70710695",
"0.70611405",
"0.7042548",
"0.70099187",
"0.7007318",
"0.70027375",
"0.7001805",
"0.6997621... | 0.73311967 | 6 |
generate 2 random numbers to add get input as addition answer check if correct, if right countdown to get 3 in a row right to end program if wrong lets keep adding and restart the 3 in a row count down | def main():
min_random = 10 #keeping constant for the min random number range
max_random = 99 #keeping constant for the max random number range
count = 0 #creating a counter variable to keep track of user's answers in a row
while count != 3: #this loop will keep goin until user get 3 answers correct i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n correct = 0\n\n while correct < GOAL:\n #random.seed(1)\n ##set up addition problem:\n num1 = random.randint(RAND_MIN, RAND_MAX)\n num2 = random.randint(RAND_MIN, RAND_MAX)\n ans = num1 + num2\n\n ##print and solve addition problem:\n print(\"Wha... | [
"0.73614657",
"0.70625",
"0.6931597",
"0.67691684",
"0.65652305",
"0.6021567",
"0.5964148",
"0.5895458",
"0.58120376",
"0.5797901",
"0.5791938",
"0.5759847",
"0.57452613",
"0.57439905",
"0.573757",
"0.56791544",
"0.5662617",
"0.56588566",
"0.5658514",
"0.5648855",
"0.5645049"... | 0.75160843 | 0 |
split a list into two lists | def split_array(a):
n = len(a)
if n == 1:
return a
index = n // 2
b = a[:index]
c = a[index:]
return b, c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_list(a_list):\n half = len(a_list)/2\n return a_list[:half], a_list[half:]",
"def split(list):\r\n \r\n mid = len(list)//2\r\n left = list[:mid]\r\n right = list[mid:]\r\n \r\n return left, right",
"def split_list(input_list):\n if len(input_list) % 2 == 0:\n half = ... | [
"0.8012361",
"0.78058696",
"0.77976507",
"0.7795386",
"0.7776519",
"0.76878697",
"0.7570118",
"0.74386173",
"0.73480934",
"0.73293114",
"0.70478654",
"0.689611",
"0.6859675",
"0.68518037",
"0.6849828",
"0.6820981",
"0.68165535",
"0.67306584",
"0.67293876",
"0.6690479",
"0.663... | 0.6365236 | 33 |
count the number of inversions | def countArrary(input_a):
if len(input_a) == 1:
return 0
else:
# split the input array
split_a = [input_a]
while len(split_a) != len(input_a):
new_split_a = []
for sub_a in split_a:
if len(sub_a) > 1:
b, c = split_array(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __count_inversions(puzzle):\n puzzleLength = len(puzzle)\n count = 0\n for i in range(puzzleLength):\n for j in range(i + 1, puzzleLength):\n if(puzzle[i] > puzzle[j]):\n count += 1\n return count",
"def inversions(state):\r\n state_copy = state.copy()\r\n s... | [
"0.7918705",
"0.7557642",
"0.7374657",
"0.73435336",
"0.72812754",
"0.7223587",
"0.7174155",
"0.71684355",
"0.6687496",
"0.66617197",
"0.6377674",
"0.63157755",
"0.6259199",
"0.6219069",
"0.614649",
"0.613124",
"0.6001056",
"0.5896113",
"0.5847523",
"0.58374596",
"0.5817897",... | 0.0 | -1 |
Internal setattr method to set new parameters, only used to fill the parameters that need to be computed right after initialization | def _set_param(self, name, value):
self._frozenjson._data[name] = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setattr__(self, name, value):\n if name in ['parameters', 'program_name']: # Allowed attributes\n self.__dict__[name] = value\n else:\n self.set_parameter(name, value) # treat as a parameter",
"def __setattr__(self,name,val):\n # use dir() not hasattr() because h... | [
"0.7481355",
"0.7261963",
"0.72144943",
"0.71701014",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
"0.70615095",
... | 0.0 | -1 |
Upcoming system to show deaths that level, time taken, etc. | def loadingScreen(self):
self.continueButton = pygame.image.load(Directory().get_directory() + '/images/intro/play.png')
self.continueButton2 = pygame.image.load(Directory().get_directory() + '/images/intro/play2.png')
# pygame.display.set_caption("Master of Thieves")
self.background_im... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_deaths(self, db_session):\n deaths = self._get_current_deaths(db_session)\n total_deaths = self._get_total_deaths(db_session)\n self._add_to_chat_queue(\"Current Boss Deaths: {}, Total Deaths: {}\".format(deaths, total_deaths))",
"def death(self):\n print \"{0} has died, like... | [
"0.679813",
"0.67267483",
"0.6689272",
"0.66167647",
"0.62606406",
"0.6160424",
"0.61004",
"0.6084413",
"0.6072137",
"0.5954452",
"0.59115213",
"0.5863395",
"0.5815567",
"0.5803057",
"0.5776313",
"0.57433033",
"0.5698685",
"0.563933",
"0.5636597",
"0.5630961",
"0.5609592",
... | 0.0 | -1 |
Checks if a path is an actual directory | def is_dir(dirname):
if not os.path.isdir(dirname):
msg = "{0} is not a directory".format(dirname)
raise argparse.ArgumentTypeError(msg)
else:
return dirname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_dir(self, path: PathLike):",
"def is_dir(self, path):",
"def isdir(path):\n system = get_instance(path)\n\n # User may use directory path without trailing '/'\n # like on standard file systems\n return system.isdir(system.ensure_dir_path(path))",
"def is_directory(path: str) -> bool:\n ... | [
"0.8388225",
"0.8251169",
"0.81738156",
"0.8151933",
"0.81462157",
"0.8120222",
"0.8118569",
"0.80518216",
"0.80454",
"0.78701305",
"0.77093875",
"0.7688265",
"0.7653269",
"0.76149786",
"0.7586614",
"0.7580526",
"0.75637823",
"0.75249004",
"0.7503654",
"0.7503654",
"0.7503189... | 0.6668291 | 68 |
Set up test fixtures, if any. | def setUp(self): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fixture_setup(self):\n pass",
"def setUp(self):\n self.app = load_app(self.application_under_test)\n\n try:\n teardown_db()\n except Exception as e:\n print('-> err ({})'.format(e.__str__()))\n\n setup_app(section_name=self.application_under_test)\n ... | [
"0.8249083",
"0.8189046",
"0.7988954",
"0.7984824",
"0.76216614",
"0.75593793",
"0.75358236",
"0.7493183",
"0.74836344",
"0.74836344",
"0.7477272",
"0.744775",
"0.744392",
"0.7414002",
"0.74075687",
"0.7363016",
"0.7358137",
"0.73313785",
"0.73291314",
"0.73162884",
"0.730626... | 0.0 | -1 |
Tear down test fixtures, if any. | def tearDown(self): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n super(TestSelectAPI, self).tearDown()\n self.destroy_fixtures()",
"def tearDown(self):\n try:\n os.remove(self.fixture_file)\n except OSError:\n pass",
"def tearDown(self):\n try:\n os.remove(self.fixtureFile)\n ex... | [
"0.7995231",
"0.77208245",
"0.7695707",
"0.7695625",
"0.7575251",
"0.7575251",
"0.75296205",
"0.7503916",
"0.74814683",
"0.74688035",
"0.7423698",
"0.74077475",
"0.74077475",
"0.74077475",
"0.7386357",
"0.73848885",
"0.73664594",
"0.73431826",
"0.73431826",
"0.73431826",
"0.7... | 0.0 | -1 |
Loads performance data Returns PD DataFrame | def pd_load_performance_csv(performance_path, **kwargs):
cols = [
"loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb",
"loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity",
"maturity_date", "msa", "current_loan_delinquency... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_pandas():\n data = _get_data()\n return du.process_pandas(data, endog_idx=0)",
"def load_pandas():\n data = _get_data()\n return du.process_pandas(data, endog_idx=0)",
"def load():\n return load_pandas()",
"def load_pandas():\n data = _get_data()\n return du.process_pandas(data,... | [
"0.7106588",
"0.7106588",
"0.69746447",
"0.6966801",
"0.66129994",
"0.6610344",
"0.6602571",
"0.64439434",
"0.6350712",
"0.63452655",
"0.63425106",
"0.6312122",
"0.629684",
"0.62943643",
"0.62704605",
"0.6257573",
"0.6243574",
"0.6237409",
"0.6233404",
"0.62208784",
"0.622038... | 0.64653116 | 7 |
Loads acquisition data Returns PD DataFrame | def pd_load_acquisition_csv(acquisition_path, **kwargs):
columns = [
'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term',
'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score',
'first_home_buyer', 'lo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pd_load_acquisition_csv(acquisition_path, **kwargs):\n\n cols = [\n 'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term',\n 'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score',\n 'first_home_buye... | [
"0.6419686",
"0.6402017",
"0.63575786",
"0.62864983",
"0.6271366",
"0.62537175",
"0.6238884",
"0.62184477",
"0.61879724",
"0.6175933",
"0.6175492",
"0.61639774",
"0.61547565",
"0.6142899",
"0.6125022",
"0.60660636",
"0.60647833",
"0.60528654",
"0.6033451",
"0.60246754",
"0.60... | 0.6446527 | 0 |
Loads names used for renaming the banks Returns PD DataFrame | def pd_load_names(**kwargs):
cols = [
'seller_name', 'new'
]
dtypes = {'seller_name':str, 'new':str}
return pd.read_csv(os.path.join(data_directory, "names.csv"), names=cols, delimiter='|', dtype=dtypes) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def simpleColumnNames():\n global masterdf\n\n df = masterdf.copy()\n #df = df[:int(len(df)*percentdata*0.01)]\n # new collumn names otherwise create_indicators break\n # [OPEN-HIGH-LOW-CLOSE-TICKVOL-VOL]\n # O-H-L-C-T-V-S colum suffixes\n newnames = [ symbols[i]+'_'+masterdf.columns[j][0]\n ... | [
"0.5787951",
"0.56984186",
"0.5362771",
"0.5320787",
"0.52894",
"0.5230477",
"0.5216697",
"0.5202509",
"0.52011865",
"0.5184204",
"0.5181308",
"0.5171009",
"0.5170913",
"0.5133046",
"0.51167834",
"0.5063004",
"0.5050153",
"0.50478053",
"0.5040606",
"0.5038574",
"0.50367355",
... | 0.5691099 | 2 |
Simple permission fix for read only files. | def __shutil_fix(func, path, exc):
# If the function is rmdir, remove or unlink and is an access error
if func in (os.rmdir, os.remove, os.unlink) and exc[1].errno == errno.EACCES:
# Set 777 as the permissions and call the function again
os.chmod(path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_file_perms():\n yield\n os.chmod('tackle.yaml', int('0o644', 8))",
"def edit_file_permission(request, app=None, priv=None):\n pass",
"def chmod_file ( self, fspath ):\n return",
"def _ensure_read_write_access(tarfileobj):\n dir_perm = tarfile.TUREAD | tarfile.TUWRITE | tarfile.TUEXEC\n... | [
"0.75999486",
"0.70999444",
"0.7047277",
"0.7023621",
"0.69472337",
"0.6907189",
"0.6885115",
"0.68610114",
"0.68281835",
"0.6725736",
"0.66714793",
"0.6639541",
"0.66357964",
"0.6629603",
"0.65960824",
"0.6583971",
"0.65828264",
"0.6509163",
"0.6497966",
"0.6458645",
"0.6458... | 0.63029516 | 24 |
Alternative version of rmtree with support for removing read only files. | def rmtree(path, ignore_errors=False):
shutil.rmtree(path, ignore_errors, __shutil_fix) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rmtree(path: str) -> None:\n def handle_remove_readonly(\n func: Callable[..., Any],\n path: str,\n exc: tuple[type[OSError], OSError, TracebackType],\n ) -> None:\n excvalue = exc[1]\n if (\n func in (os.rmdir, os.remove, os.unlink) and\n ... | [
"0.80030376",
"0.7450629",
"0.721649",
"0.7020184",
"0.68477285",
"0.68477285",
"0.6693467",
"0.6554252",
"0.65185106",
"0.6416231",
"0.6387251",
"0.63817346",
"0.6379617",
"0.6351209",
"0.63437504",
"0.6283398",
"0.62554175",
"0.61940765",
"0.6185127",
"0.6165985",
"0.615647... | 0.6368275 | 13 |
Reads Intan Technologies RHD2000 data file generated by evaluation board GUI. Data are returned in a dictionary, for future extensibility. | def read_data(filename):
from intanutil.read_header import read_header
from intanutil.get_bytes_per_data_block import get_bytes_per_data_block
from intanutil.read_one_data_block import read_one_data_block
from intanutil.notch_filter import notch_filter
from intanutil.data_to_result import data_to_re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_header(fid):\r\n\r\n # Check 'magic number' at beginning of file to make sure this is an Intan\r\n # Technologies RHD2000 data file.\r\n magic_number, = struct.unpack('<I', fid.read(4)) \r\n if magic_number != int('c6912702', 16): raise Exception('Unrecognized file type.')\r\n\r\n header = ... | [
"0.6393038",
"0.6149901",
"0.5831465",
"0.5748882",
"0.57278347",
"0.57030857",
"0.5688642",
"0.5681411",
"0.56662315",
"0.56551236",
"0.5647856",
"0.5566591",
"0.55629826",
"0.55400455",
"0.5536587",
"0.5534342",
"0.5492185",
"0.54791164",
"0.5452682",
"0.54502016",
"0.54300... | 0.5347599 | 30 |
Utility function to optionally pluralize words based on the value of n. | def plural(n):
if n == 1:
return ''
else:
return 's' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pluralize(n, s):\n if n == 1:\n return f'1 {s}'\n else:\n return f'{n} {s}s'",
"def plural(n):\n if n != 1:\n return \"s\"\n else:\n return \"\"",
"def plural(num, one, many):\n\n return \"%i %s\" % (num, one if num == 1 else many)",
"def pluralize(word, num):\n... | [
"0.74885666",
"0.7429422",
"0.7037012",
"0.6609908",
"0.6518552",
"0.64686424",
"0.6346233",
"0.63218504",
"0.6162843",
"0.6089325",
"0.6033861",
"0.60118306",
"0.59906334",
"0.5960944",
"0.59445107",
"0.5938011",
"0.5879026",
"0.5834877",
"0.5833",
"0.58170116",
"0.58166254"... | 0.7405934 | 2 |
quote the elements of a dotted name | def quote_dotted(
name: Union["quoted_name", str], quote: functools.partial
) -> Union["quoted_name", str]:
if isinstance(name, quoted_name):
return quote(name)
result = ".".join([quote(x) for x in name.split(".")])
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dotted_name(s):\n forbidden = forbidden_chars.intersection(s)\n if forbidden:\n raise ValueError('%(s)s contains forbidden characters'\n ' (%(forbidden)s)'\n % locals())\n if not s:\n return ''\n elif s in reserved_names:\n raise ValueError('The na... | [
"0.6540108",
"0.64451617",
"0.6417363",
"0.6304107",
"0.58152014",
"0.5777598",
"0.5726563",
"0.57090414",
"0.5696863",
"0.5691718",
"0.56322443",
"0.550168",
"0.5483858",
"0.5444921",
"0.54370934",
"0.5433207",
"0.53710306",
"0.53705055",
"0.5365929",
"0.5365884",
"0.5363487... | 0.745443 | 0 |
Convert text to float or 0.0 if invalid. | def convert_to_number(text):
try:
value = float(text)
return value
except ValueError:
return 0.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ffloat(string):\n try:\n return float(string.strip())\n except:\n return 0",
"def _convert_to_float(s):\n try:\n return float(s)\n except:\n return s",
"def safe_float(str):\n if not str:\n return None\n try:\n return float(str)\n ... | [
"0.7549978",
"0.7463989",
"0.73810375",
"0.73316866",
"0.7268213",
"0.7146606",
"0.7116986",
"0.7115148",
"0.7079289",
"0.7048062",
"0.69954073",
"0.6994393",
"0.69855756",
"0.69264966",
"0.6895625",
"0.68766963",
"0.68540186",
"0.68069357",
"0.679633",
"0.6766438",
"0.674790... | 0.7922715 | 0 |
Implements the kNN classifer to classify the testing dataset based on the training dataset | def predictTest(k, train, test):
pred_labels = []
# for each instance in the testing dataset, calculate all L2 distance from all training instances
for te in range(len(test)):
all_D = np.zeros((len(train), 1))
# calculate the L2 distance of the testing instance from each training ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_knn(training_data):\n return knnclassifier(training_data, keys, 3)",
"def KNN(x_train, x_test, y_train, k=3):\n knn = KNeighborsClassifier(n_neighbors=k)\n knn.fit(x_train, y_train)\n y_pred = knn.predict(x_test)\n return y_pred",
"def knn(train_data, train_labels, test_data, test_labe... | [
"0.81317216",
"0.8076582",
"0.79367137",
"0.7887092",
"0.76906496",
"0.76182103",
"0.7588983",
"0.7570624",
"0.74730897",
"0.74619097",
"0.7450934",
"0.7319036",
"0.72715604",
"0.72668743",
"0.7201178",
"0.7107569",
"0.7090845",
"0.70277756",
"0.70149493",
"0.6955066",
"0.692... | 0.7077485 | 17 |
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