path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34130031/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34130031/cell_7 | [
"text_plain_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
train_annotations.keys() | code |
34130031/cell_8 | [
"text_html_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_test_information.json', 'r', errors='ignore') as f:
test_information = json.load(f)
... | code |
34130031/cell_15 | [
"text_plain_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submissio... | code |
34130031/cell_16 | [
"text_plain_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
train_annotations.keys()
train_annotations['info'] | code |
34130031/cell_17 | [
"text_html_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_test_information.json', 'r', errors='ignore') as f:
test_information = json.load(f)
... | code |
316827/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shar... | code |
316827/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_20 | [
"text_plain_output_1.png"
] | from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', ... | code |
316827/cell_40 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns =... | code |
316827/cell_43 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_24 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_27 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
316827/cell_37 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby(... | code |
16161648/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from tensorflow.ker... | code |
16161648/cell_2 | [
"text_plain_output_1.png"
] | import os
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16161648/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinP... | code |
16161648/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read... | code |
16161648/cell_7 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinP... | code |
16161648/cell_16 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
nn_model = Sequential()
nn_model.add(Dense(20, activation='relu', input_shape=(20,)))
nn_model.add(Dropout(0.3, noise_shap... | code |
16161648/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
16161648/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as ... | code |
16161648/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. ... | code |
122249481/cell_13 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_l... | code |
122249481/cell_4 | [
"image_output_1.png"
] | df_X = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/training_set_rna.csv', index_col=0).T
df = df_X
df_Y = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/training_set_adt.csv', index_col=0).T
df_X_submission = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/t... | code |
122249481/cell_20 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD... | code |
122249481/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
t0start = time.time()
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname... | code |
122249481/cell_19 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD... | code |
122249481/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize=(20, 8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA', 'CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_l... | code |
122249481/cell_8 | [
"text_plain_output_1.png"
] | df_corr = df.corr()
N = 20
d = df_corr['CD53'].sort_values(ascending=False, key=abs).head(N).to_frame()
for t in df_corr['CD53'].sort_values(ascending=False, key=abs).index[:N]:
m = (df[t] != 0) & (df['CD53'] != 0)
c = np.corrcoef(df[t][m], df['CD53'][m])[0, 1]
d.loc[t, 'Corr non zeros'] = c
d | code |
122249481/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD... | code |
122249481/cell_17 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD... | code |
122249481/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_l... | code |
122249481/cell_10 | [
"text_plain_output_1.png"
] | import umap
reducer = umap.UMAP(random_state=42)
r = reducer.fit_transform(df)
dict_reds = {}
dict_reds['umap'] = r
n_x_subplots = 2
c = 0
str_data_inf = 'CITEseq2302'
l = ['CD45RA', 'CD45RO', 'PTPRC', 'CD53', 'MALAT1', 'NEAT1', 'CD3', 'CD4', 'CD69']
for gene in l[:40]:
if gene in df.columns:
v4color = df[g... | code |
122249481/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_l... | code |
122249481/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from scipy import stats
d1_corr = pd.DataFrame(index=df.columns)
res = stats.pearsonr([1, 2, 3, 4, 5], [10, 9, 2.5, 6, 4])
col1 = 'CD45RA'
for col in df.columns:
v0 = df[col]
v1 = df_Y[col1]
res = stats.pearsonr(v0, v1)
d1_corr.loc[col, 'Corr ' + col1] = res[0]
d1_corr.loc[col, 'pvalue ' + col1] = r... | code |
16125229/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_... | code |
16125229/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
state.columns | code |
16125229/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.... | code |
16125229/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_... | code |
16125229/cell_6 | [
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns | code |
16125229/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.... | code |
16125229/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
hits.head() | code |
16125229/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_... | code |
16125229/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
musics.columns | code |
16125229/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
genre.columns | code |
16125229/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').... | code |
16125229/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').... | code |
16125229/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').... | code |
16125229/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').... | code |
16125229/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
musics.columns
musics.head() | code |
16125229/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.... | code |
90118469/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import random
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.C... | code |
90118469/cell_13 | [
"text_plain_output_1.png"
] | X_test_final = X_test.to_numpy(dtype='uint8')
print(X_test_final) | code |
90118469/cell_9 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_outp... | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape | code |
90118469/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixD... | code |
90118469/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixD... | code |
90118469/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(y_pred.shape) | code |
90118469/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixD... | code |
90118469/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape | code |
90118469/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.t... | code |
90118469/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.t... | code |
90118469/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_train.head() | code |
90118469/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.t... | code |
90118469/cell_10 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
print(f'Classification report for classifier {lda}:\n{metrics.cl... | code |
17121374/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
print(test_df.shape)
test_df.describe() | code |
17121374/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.head(10) | code |
17121374/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df... | code |
17121374/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns | code |
17121374/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df.isnull().sum() / 1460 * 100).iloc[0:50]
(test_df.isnull().... | code |
17121374/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17121374/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes | code |
17121374/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df.isnull().sum() / 1460 * 100).iloc[0:50] | code |
17121374/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
(test_df.isnull().sum() / 1460 * 100).iloc[50:82] | code |
17121374/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
print(train_df.shape)
train_df.describe() | code |
17121374/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
test_df.head(10) | code |
48163942/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display... | code |
48163942/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display... | code |
48163942/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display... | code |
48163942/cell_46 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display... | code |
48163942/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option... | code |
48163942/cell_5 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33118743/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
33118743/cell_4 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_sha... | code |
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