code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import pandas as pd
from rdkit.Chem import GetDistanceMatrix
from rdkit.DataStructs import ConvertToNumpyArray
from rdkit.Chem.rdMolDescriptors import (GetMorganFingerprint,
GetHashedMorganFingerprint,
GetMorganFingerprintAsBitVect,
... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/descriptors/fingerprints.py | 0.821582 | 0.460107 | fingerprints.py | pypi |
import matplotlib.pyplot as plt
from .. import descriptors
from .. import core
from .. import vis
from ipywidgets import Dropdown, Text, VBox, HBox, Valid, HTML
from IPython import get_ipython
from IPython.display import clear_output, display
class Visualizer(object):
def __init__(self, fper='morgan', smiles='c... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/interact/desc_vis.py | 0.619817 | 0.283019 | desc_vis.py | pypi |
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
import pandas as pd
class ChemicalObject(object):
""" A mixin for each chemical object in scikit-chem """
@classmethod
def from_super(cls, obj):
"""A method that converts the class of an object of parent class to that of... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/core/base.py | 0.868213 | 0.356251 | base.py | pypi |
import warnings
import tempfile
import os
import pandas as pd
from fuel.datasets import H5PYDataset
from fuel.utils import find_in_data_path
from fuel import config
class Dataset(H5PYDataset):
""" Abstract base class providing an interface to the skchem data format."""
def __init__(self, **kwargs):
... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/datasets/base.py | 0.829699 | 0.334141 | base.py | pypi |
import warnings
import logging
import os
from collections import namedtuple
import numpy as np
import pandas as pd
import h5py
from fuel.datasets import H5PYDataset
from ... import forcefields
from ... import filters
from ... import descriptors
from ... import standardizers
from ... import pipeline
logger = logging.... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/base.py | 0.787768 | 0.400046 | base.py | pypi |
import zipfile
import os
import logging
LOGGER = logging.getLogger(__name__)
import numpy as np
import pandas as pd
from .base import Converter, default_pipeline
from ... import io
from ... import core
class Tox21Converter(Converter):
""" Class to build tox21 dataset.
"""
def __init__(self, directory, ... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/tox21.py | 0.53048 | 0.389779 | tox21.py | pypi |
import os
import logging
import itertools
from collections import defaultdict
import pandas as pd
import numpy as np
from sklearn import metrics
from .base import Converter, default_pipeline, contiguous_order
from ... import io
from ... import utils
from ...cross_validation import SimThresholdSplit
LOGGER = logging... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/nmrshiftdb2.py | 0.657098 | 0.379838 | nmrshiftdb2.py | pypi |
import os
import zipfile
import logging
LOGGER = logging.getLogger(__name__)
import pandas as pd
import numpy as np
import skchem
from .base import Converter
from ... import standardizers
PATCHES = {
'820-75-7': r'NNC(=O)CNC(=O)C=[N+]=[N-]',
'2435-76-9': r'[N-]=[N+]=C1C=NC(=O)NC1=O',
'817-99-2': r'NC(=... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/muller_ames.py | 0.512937 | 0.327319 | muller_ames.py | pypi |
import os
import zipfile
import logging
LOGGER = logging.getLogger(__name__)
import pandas as pd
import numpy as np
from ... import io
from .base import Converter, contiguous_order
from ...cross_validation import SimThresholdSplit
TXT_COLUMNS = [l.lower() for l in """CAS
Formula
Mol_Weight
Chemical_Name
WS
WS_temp... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/physprop.py | 0.417865 | 0.346514 | physprop.py | pypi |
import os
import logging
logger = logging.getLogger(__name__)
import pandas as pd
from .base import Converter, default_pipeline, contiguous_order
from ...core import Mol
from ...cross_validation import SimThresholdSplit
class BradleyOpenMPConverter(Converter):
def __init__(self, directory, output_directory, ou... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/data/converters/bradley_open_mp.py | 0.514888 | 0.241775 | bradley_open_mp.py | pypi |
from functools import wraps
import warnings
from rdkit import Chem
import pandas as pd
from ..core import Mol
from ..utils import Suppressor, squeeze
def _drop_props(row):
for prop in row.structure.props.keys():
row.structure.ClearProp(prop)
def _set_props(row, cols):
for i in cols:
row.stru... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/io/sdf.py | 0.749271 | 0.449574 | sdf.py | pypi |
from collections import Counter
import numpy as np
import pandas as pd
from ..resource import ORGANIC, PERIODIC_TABLE
from .base import Filter
class ElementFilter(Filter):
""" Filter by elements.
Args:
elements (list[str]):
A list of elements to filter with. If an element ... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/filters/simple.py | 0.896438 | 0.505188 | simple.py | pypi |
from rdkit import RDConfig
import os
import pandas as pd
from .base import Filter
from ..core import Mol
class SMARTSFilter(Filter):
""" Filter a molecule based on smarts.
Args:
smarts (pd.Series):
A series of SMARTS to use in the filter.
agg (function):
Option specif... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/filters/smarts.py | 0.837985 | 0.489198 | smarts.py | pypi |
from rdkit.Chem.Draw import MolToImage, DrawingOptions
import numpy as np
from matplotlib import pyplot as plt
def plot_weights(mol, weights, quality=1, l=0.4, step=50, levels=20, contour_opacity=0.5, cmap='RdBu', ax=None, **kwargs):
""" Plot weights as a sum of gaussians across a structure image.
Args:
... | /scikit-chem-0.0.6.tar.gz/scikit-chem-0.0.6/skchem/vis/atom.py | 0.967506 | 0.670804 | atom.py | pypi |
import argparse
import ci
import os
class _OptionalStep(argparse.Action):
"""Custom action making the ``step`` positional argument with choices
optional.
Setting the ``choices`` attribute will fail with an *invalid choice* error.
Adapted from http://stackoverflow.com/questions/8526675/python-argpars... | /scikit-ci-0.21.0.tar.gz/scikit-ci-0.21.0/ci/__main__.py | 0.583559 | 0.174621 | __main__.py | pypi |
Scikit-clean
==================
**scikit-clean** is a python ML library for classification in the presence of \
label noise. Aimed primarily at researchers, this provides implementations of \
several state-of-the-art algorithms; tools to simulate artificial noise, create complex pipelines \
and evaluate them.
This li... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/README.rst | 0.875121 | 0.862178 | README.rst | pypi |
## Introduction to Scikit-clean
`scikit-clean` is a python ML library for classification in the presence of label noise. Aimed primarily at researchers, this provides implementations of several state-of-the-art algorithms, along with tools to simulate artificial noise, create complex pipelines and evaluate them.
### ... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/examples/Introduction to Scikit-clean.ipynb | 0.844601 | 0.986585 | Introduction to Scikit-clean.ipynb | pypi |
## Evaluating Detectors
In `scikit-clean`, A `Detector` only identifies/detects the mislabelled samples. It's not a complete classifier (rather a part of one). So procedure for their evaluation is different.
We can view a noise detector as a binary classifier: it's job is to provide a probability denoting if a sample... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/examples/Evaluating Detectors.ipynb | 0.634996 | 0.881564 | Evaluating Detectors.ipynb | pypi |
## Evaluating Robust Models
The goal of this notebook is to show how to compare several methods across several datasets.This will also serve as inroduction to two important `scikit-clean` functions: `load_data` and `compare`.
We'll (roughly) implement the core idea of 3 existing papers on robust classification in th... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/examples/Evaluating Robust Methods.ipynb | 0.447943 | 0.970882 | Evaluating Robust Methods.ipynb | pypi |
## Introduction to Scikit-clean
`scikit-clean` is a python ML library for classification in the presence of label noise. Aimed primarily at researchers, this provides implementations of several state-of-the-art algorithms, along with tools to simulate artificial noise, create complex pipelines and evaluate them.
### ... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/doctrees/nbsphinx/examples/Introduction to Scikit-clean.ipynb | 0.844601 | 0.986585 | Introduction to Scikit-clean.ipynb | pypi |
## Evaluating Detectors
In `scikit-clean`, A `Detector` only identifies/detects the mislabelled samples. It's not a complete classifier (rather a part of one). So procedure for their evaluation is different.
We can view a noise detector as a binary classifier: it's job is to provide a probability denoting if a sample... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/doctrees/nbsphinx/examples/Evaluating Detectors.ipynb | 0.634996 | 0.881564 | Evaluating Detectors.ipynb | pypi |
## Evaluating Robust Models
The goal of this notebook is to show how to compare several methods across several datasets.This will also serve as inroduction to two important `scikit-clean` functions: `load_data` and `compare`.
We'll (roughly) implement the core idea of 3 existing papers on robust classification in th... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/doctrees/nbsphinx/examples/Evaluating Robust Methods.ipynb | 0.447943 | 0.970882 | Evaluating Robust Methods.ipynb | pypi |
## Introduction to Scikit-clean
`scikit-clean` is a python ML library for classification in the presence of label noise. Aimed primarily at researchers, this provides implementations of several state-of-the-art algorithms, along with tools to simulate artificial noise, create complex pipelines and evaluate them.
### ... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/html/examples/Introduction to Scikit-clean.ipynb | 0.844601 | 0.986585 | Introduction to Scikit-clean.ipynb | pypi |
## Evaluating Detectors
In `scikit-clean`, A `Detector` only identifies/detects the mislabelled samples. It's not a complete classifier (rather a part of one). So procedure for their evaluation is different.
We can view a noise detector as a binary classifier: it's job is to provide a probability denoting if a sample... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/html/examples/Evaluating Detectors.ipynb | 0.634996 | 0.881564 | Evaluating Detectors.ipynb | pypi |
## Evaluating Robust Models
The goal of this notebook is to show how to compare several methods across several datasets.This will also serve as inroduction to two important `scikit-clean` functions: `load_data` and `compare`.
We'll (roughly) implement the core idea of 3 existing papers on robust classification in th... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/doc/_build/html/examples/Evaluating Robust Methods.ipynb | 0.447943 | 0.970882 | Evaluating Robust Methods.ipynb | pypi |
import numpy as np
from scipy.stats import entropy
from sklearn.base import BaseEstimator, TransformerMixin, clone
from sklearn.preprocessing import minmax_scale
from sklearn.utils import check_random_state
from skclean.utils.noise_generation import gen_simple_noise_mat
def _flip_idx(Y, target_idx, random_state=None... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/simulate_noise.py | 0.899096 | 0.57523 | simulate_noise.py | pypi |
import warnings
import numpy as np
from sklearn.base import ClassifierMixin, clone
from sklearn.model_selection import StratifiedKFold
from sklearn.utils import shuffle, check_random_state
from .base import BaseHandler
class Filter(BaseHandler, ClassifierMixin):
"""
Removes from dataset samples most likely t... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/handlers/filters.py | 0.842896 | 0.552298 | filters.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils import check_random_state
from ..detectors.base import BaseDetector
from sklearn.utils.validation import _check_sample_weight
def _check_data_params(obj, X, y, conf_score):
"""Extracted out of BaseHandler for WeightedBa... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/handlers/base.py | 0.757346 | 0.243187 | base.py | pypi |
import warnings
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import check_random_state
from skclean.handlers.base import BaseHandler, _check_data_params
class SampleWeight(... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/handlers/example_weighting.py | 0.92222 | 0.453625 | example_weighting.py | pypi |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.base import ClassifierMixin, BaseEstimator
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors._base import _get_weights
from sklearn.utils.extmath import weighted_mode
# TODO: support all sklearn Random Forest parameters
... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/models/ensemble.py | 0.764892 | 0.613787 | ensemble.py | pypi |
import numpy as np
from scipy.optimize import minimize
from sklearn.linear_model import LogisticRegression
from sklearn.utils.extmath import log_logistic
from sklearn.utils.multiclass import unique_labels
def log_loss(wp, X, target, C, PN, NP):
"""
It is minimized using "L-BFGS-B" method of "scipy.optimize.m... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/models/logistic_regression.py | 0.924959 | 0.627438 | logistic_regression.py | pypi |
import numpy as np
from sklearn.exceptions import NotFittedError
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors._base import _get_weights
from .base import BaseDetector
# TODO: Support other distance metrics
class KDN(BaseDetector):
"... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/detectors/neighbors.py | 0.787073 | 0.54056 | neighbors.py | pypi |
import warnings
import numpy as np
from sklearn import clone
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/detectors/ensemble.py | 0.870308 | 0.495117 | ensemble.py | pypi |
import numpy as np
from sklearn.utils import check_random_state
def noise_matrix_is_valid(noise_matrix, py, verbose=False):
'''Given a prior py = p(y=k), returns true if the given noise_matrix is a learnable matrix.
Learnability means that it is possible to achieve better than random performance, on average,... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/utils/noise_generation.py | 0.905396 | 0.711657 | noise_generation.py | pypi |
from pathlib import Path
from time import ctime, perf_counter
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, check_cv
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from sklearn.utils import shuffle, check_random_state
_intervals = (
('weeks', 604800), #... | /scikit-clean-0.1.2.tar.gz/scikit-clean-0.1.2/skclean/utils/_utils.py | 0.459804 | 0.434341 | _utils.py | pypi |
import numpy as np
from scipy.spatial.distance import cdist
from .initialization import initialize_random, initialize_probabilistic
class CMeans:
"""Base class for C-means algorithms.
Parameters
----------
n_clusters : int, optional
The number of clusters to find.
n_init : int, optional
... | /scikit-cmeans-0.1.tar.gz/scikit-cmeans-0.1/skcmeans/algorithms.py | 0.950423 | 0.63484 | algorithms.py | pypi |
[](https://secure.travis-ci.org/veeresht/CommPy)
[](https://coveralls.io/r/veeresht/CommPy)
[](https://badge.... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/README.md | 0.494629 | 0.960768 | README.md | pypi |
import numpy as np
__all__=['rcosfilter', 'rrcosfilter', 'gaussianfilter', 'rectfilter']
def rcosfilter(N, alpha, Ts, Fs):
"""
Generates a raised cosine (RC) filter (FIR) impulse response.
Parameters
----------
N : int
Length of the filter in samples.
alpha : float
Roll off f... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/filters.py | 0.928813 | 0.747363 | filters.py | pypi |
from bisect import insort
import matplotlib.pyplot as plt
from numpy import arange, array, zeros, pi, sqrt, log2, argmin, \
hstack, repeat, tile, dot, shape, concatenate, exp, \
log, vectorize, empty, eye, kron, inf, full, abs, newaxis, minimum, clip, fromiter
from numpy.fft import fft, ifft
from numpy.linalg ... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/modulation.py | 0.893768 | 0.657703 | modulation.py | pypi |
from __future__ import division # Python 2 compatibility
import math
from fractions import Fraction
from inspect import getfullargspec
import numpy as np
from commpy.channels import MIMOFlatChannel
__all__ = ['link_performance', 'LinkModel', 'idd_decoder']
def link_performance(link_model, SNRs, send_max, err_min... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/links.py | 0.939345 | 0.637313 | links.py | pypi |
from __future__ import division, print_function # Python 2 compatibility
from numpy import abs, sqrt, sum, zeros, identity, hstack, einsum, trace, kron, absolute, fromiter, array, exp, \
pi, cos
from numpy.random import randn, random, standard_normal
from scipy.linalg import sqrtm
__all__ = ['SISOFlatChannel', '... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/channels.py | 0.963882 | 0.587766 | channels.py | pypi |
__all__ = ['pnsequence', 'zcsequence']
import numpy as np
from numpy import empty, exp, pi, arange, int8, fromiter, sum
def pnsequence(pn_order, pn_seed, pn_mask, seq_length):
"""
Generate a PN (Pseudo-Noise) sequence using a Linear Feedback Shift Register (LFSR).
Seed and mask are ordered so that:
... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/sequences.py | 0.906424 | 0.68225 | sequences.py | pypi |
import functools
import numpy as np
__all__ = ['dec2bitarray', 'decimal2bitarray', 'bitarray2dec', 'hamming_dist', 'euclid_dist', 'upsample',
'signal_power']
vectorized_binary_repr = np.vectorize(np.binary_repr)
def dec2bitarray(in_number, bit_width):
"""
Converts a positive integer or an array-... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/utilities.py | 0.901146 | 0.793546 | utilities.py | pypi |
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as splg
__all__ = ['build_matrix', 'get_ldpc_code_params', 'ldpc_bp_decode', 'write_ldpc_params',
'triang_ldpc_systematic_encode']
_llr_max = 500
def build_matrix(ldpc_code_params):
"""
Build the parity check and generator ma... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/channelcoding/ldpc.py | 0.795658 | 0.530723 | ldpc.py | pypi |
from __future__ import division
import functools
import math
from warnings import warn
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import PatchCollection
from commpy.utilities ... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/channelcoding/convcode.py | 0.91895 | 0.631651 | convcode.py | pypi |
from math import gcd
from numpy import array, zeros, arange, convolve, ndarray, concatenate
from commpy.utilities import dec2bitarray, bitarray2dec
__all__ = ['GF', 'polydivide', 'polymultiply', 'poly_to_string']
class GF:
"""
Defines a Binary Galois Field of order m, containing n,
where n can be a sing... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/channelcoding/gfields.py | 0.770292 | 0.576989 | gfields.py | pypi |
# Channel codes basics
## Main idea
The main idea of the channel codes can be formulated as following thesises:
- **noise immunity** of the signal should be increased;
- **redundant bits** are added for *error detection* and *error correction*;
- some special algorithms (<u>coding schemes</u>) are used for this.
<im... | /scikit-commpy-0.8.0.tar.gz/scikit-commpy-0.8.0/commpy/channelcoding/README.md | 0.565059 | 0.913484 | README.md | pypi |
import collections
import encoder
from estimator import LRWrapper, XgBoostWrapper
from utils import common
__author__ = 'jiyue'
from sklearn.metrics import confusion_matrix
from sklearn.metrics import recall_score, precision_score, accuracy_score, roc_auc_score
from sklearn.ensemble import RandomForestClassifier
from... | /scikit-credit-0.0.23.tar.gz/scikit-credit-0.0.23/scikit_credit/framework/bootstrap.py | 0.599954 | 0.198919 | bootstrap.py | pypi |
import math
from sklearn.base import TransformerMixin
from sklearn.utils.multiclass import type_of_target
import numpy as np
from scipy import stats
import pandas as pd
__author__ = 'jiyue'
class WoeEncoder(TransformerMixin):
def __init__(self,
binning_mode='ew',
bin_width=5,
... | /scikit-credit-0.0.23.tar.gz/scikit-credit-0.0.23/scikit_credit/encoder/risk_encoder.py | 0.576423 | 0.204322 | risk_encoder.py | pypi |
__author__ = 'jiyue'
import pandas as pd
import math
from sklearn.datasets import dump_svmlight_file, load_svmlight_file
from sklearn.externals.joblib import Memory
import numpy as np
mem = Memory("~/.svmmem")
def compute_missing_pct(dataframe, dtype):
dataframe.select_dtypes(include=[dtype]).describe().T \
... | /scikit-credit-0.0.23.tar.gz/scikit-credit-0.0.23/scikit_credit/utils/common.py | 0.44746 | 0.253024 | common.py | pypi |
# Changelog of Scikit-Criteria
<!-- BODY -->
## Version 0.8.3
- Fixed a bug detected on the EntropyWeighted, Now works as the literature
specifies
## Version 0.8.2
- We bring back Python 3.7 because is the version used in google.colab.
- Bugfixes in `plot.frontier` and `dominance.eq`.
## Version 0.8
- **New**... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/CHANGELOG.md | 0.871803 | 0.680574 | CHANGELOG.md | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""The Module implements utilities to build a composite decision-maker."""
# =========================================================================... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/pipeline.py | 0.932029 | 0.570212 | pipeline.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Some simple and compensatory methods."""
# =============================================================================
# IMPORTS
# ==============... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/madm/simple.py | 0.870253 | 0.68115 | simple.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Core functionalities to create madm decision-maker classes."""
# =============================================================================
# i... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/madm/_madm_base.py | 0.936825 | 0.621828 | _madm_base.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Methods based on a similarity between alternatives."""
# =============================================================================
# IMPORTS
# ... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/madm/similarity.py | 0.92222 | 0.490053 | similarity.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""SIMUS (Sequential Interactive Model for Urban Systems) Method."""
# =============================================================================
... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/madm/simus.py | 0.863478 | 0.540742 | simus.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Implementation of functionalities for convert minimization criteria into \
maximization ones."""
# ================================================... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/preprocessing/invert_objectives.py | 0.9226 | 0.600891 | invert_objectives.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Normalization through the distance to distance function."""
# =============================================================================
# IMPO... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/preprocessing/filters.py | 0.933203 | 0.509764 | filters.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Functionalities for remove negatives from criteria.
In addition to the main functionality, an MCDA agnostic function is offered
to push negatives v... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/preprocessing/push_negatives.py | 0.936037 | 0.643203 | push_negatives.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Core functionalities to create transformers."""
# =============================================================================
# IMPORTS
# =======... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/preprocessing/_preprocessing_base.py | 0.931905 | 0.496643 | _preprocessing_base.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Data abstraction layer.
This module defines the DecisionMatrix object, which internally encompasses
the alternative matrix, weights and objectives ... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/core/data.py | 0.916236 | 0.663437 | data.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Stats helper for the DecisionMatrix object."""
# =============================================================================
# IMPORTS
# =======... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/core/stats.py | 0.922343 | 0.635109 | stats.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Definition of the objectives (MIN, MAX) for the criteria."""
# =============================================================================
# IMP... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/core/objectives.py | 0.826011 | 0.292254 | objectives.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Core functionalities of scikit-criteria."""
# =============================================================================
# IMPORTS
# ===========... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/core/methods.py | 0.871434 | 0.223971 | methods.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Dominance helper for the DecisionMatrix object."""
# =============================================================================
# IMPORTS
# ====... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/core/dominance.py | 0.913551 | 0.42931 | dominance.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""The :mod:`skcriteria.datasets` module includes utilities to load \
datasets."""
# ================================================================... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/datasets/__init__.py | 0.833325 | 0.6911 | __init__.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Container object exposing keys as attributes."""
# =============================================================================
# IMPORTS
# =====... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/utils/bunch.py | 0.818918 | 0.362066 | bunch.py | pypi |
# =============================================================================
# DOCS
# =============================================================================
"""Functions for calculate and compare ranks (ordinal series)."""
# =============================================================================
# I... | /scikit-criteria-0.8.3.tar.gz/scikit-criteria-0.8.3/skcriteria/utils/rank.py | 0.926279 | 0.778607 | rank.py | pypi |
from __future__ import print_function
from string import Template
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
import numpy as np
import skcuda.misc as misc
A = 3
B = 4
C = 5
N = A * B * C
# Define a 3D array:
# x_orig = np.arange(0, N, 1, np.float64)
x_orig = n... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/demos/indexing_3d_demo.py | 0.579519 | 0.567277 | indexing_3d_demo.py | pypi |
from __future__ import print_function
from string import Template
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
import numpy as np
import skcuda.misc as misc
A = 3
B = 4
N = A * B
# Define a 2D array:
# x_orig = np.arange(0, N, 1, np.float64)
x_orig = np.asarray(... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/demos/indexing_2d_demo.py | 0.566258 | 0.570032 | indexing_2d_demo.py | pypi |
from __future__ import print_function
from string import Template
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
import numpy as np
import skcuda.misc as misc
A = 3
B = 4
C = 5
D = 6
N = A * B * C * D
# Define a 3D array:
# x_orig = np.arange(0, N, 1, np.float64)
... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/demos/indexing_4d_demo.py | 0.546496 | 0.555857 | indexing_4d_demo.py | pypi |
import numpy as np
import scipy.linalg
import skcuda.magma as magma
import time
import importlib
importlib.reload(magma)
typedict = {'s': np.float32, 'd': np.float64, 'c': np.complex64, 'z': np.complex128}
def test_cpu_gpu(N, t='z'):
"""
N : dimension
dtype : type (default complex)
"""
... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/demos/magma_all_geev_demo.py | 0.429429 | 0.508483 | magma_all_geev_demo.py | pypi |
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import pycuda.elementwise as el
from pycuda.tools import context_dependent_memoize
import pycuda.tools as tools
import numpy as np
from . import cufft
from .cufft import CUFFT_COMPATIBILITY_NATIVE, \
CUFFT_COMPATIBILITY_FFTW_PADDING, \
CUFFT_COMP... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/fft.py | 0.753557 | 0.457803 | fft.py | pypi |
import atexit
import ctypes.util
import platform
from string import Template
import sys
import warnings
import numpy as np
import cuda
# Load library:
_version_list = [10.1, 10.0, 9.2, 9.1, 9.0, 8.0, 7.5, 7.0, 6.5, 6.0, 5.5, 5.0, 4.0]
if 'linux' in sys.platform:
_libcusparse_libname_list = ['libcusparse.so'] + \... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/cusparse.py | 0.46952 | 0.20838 | cusparse.py | pypi |
import ctypes
import operator
import re
import sys
# Load library:
_linux_version_list = [10.1, 10.0, 9.2, 9.1, 9.0, 8.0, 7.5, 7.0, 6.5, 6.0, 5.5, 5.0, 4.0]
_win32_version_list = [10, 100, 92, 91, 90, 80, 75, 70, 65, 60, 55, 50, 40]
if 'linux' in sys.platform:
_libcufft_libname_list = ['libcufft.so'] + \
... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/cufft.py | 0.489748 | 0.15059 | cufft.py | pypi |
from __future__ import absolute_import, division
import atexit
import numbers
from string import Template
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import pycuda.elementwise as elementwise
import pycuda.reduction as reduction
import pycuda.scan as scan
import pycuda.tools as tools
from pycuda.too... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/misc.py | 0.607547 | 0.183155 | misc.py | pypi |
from __future__ import absolute_import, division
from pprint import pprint
from string import Template
from pycuda.tools import context_dependent_memoize
from pycuda.compiler import SourceModule
from pycuda.reduction import ReductionKernel
from pycuda import curandom
from pycuda import cumath
import pycuda.gpuarray ... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/rlinalg.py | 0.712232 | 0.461927 | rlinalg.py | pypi |
import os
import pycuda.gpuarray as gpuarray
import pycuda.elementwise as elementwise
from pycuda.tools import context_dependent_memoize
import numpy as np
from . import misc
from .misc import init
# Get installation location of C headers:
from . import install_headers
@context_dependent_memoize
def _get_sici_kernel... | /scikit-cuda-0.5.3.tar.gz/scikit-cuda-0.5.3/skcuda/special.py | 0.794066 | 0.489503 | special.py | pypi |
import typing as ty
import collections.abc as abc
import numpy as np
import scipy.signal as signal
import scipy.signal.windows as windows
import scipy.ndimage as ndimage
if ty.TYPE_CHECKING:
from curve._base import Curve
class SmoothingError(Exception):
"""Any smoothing errors
"""
_SMOOTHING_FILTERS =... | /scikit_curve-0.1.0-py3-none-any.whl/curve/_smooth.py | 0.906759 | 0.442456 | _smooth.py | pypi |
import typing as ty
import numpy as np
F_EPS = np.finfo(np.float64).eps
def isequal(obj1: np.ndarray, obj2: np.ndarray, **kwargs) -> np.ndarray:
"""Returns a boolean array where two arrays are element-wise equal
Notes
-----
int/float dtype independent equal check
Parameters
----------
... | /scikit_curve-0.1.0-py3-none-any.whl/curve/_numeric.py | 0.900124 | 0.808067 | _numeric.py | pypi |
import numpy as np
from scipy.special import fresnel
from curve import Curve
def arc(t_start: float = 0.0,
t_stop: float = np.pi * 2,
p_count: int = 49,
r: float = 1.0,
c: float = 0.0) -> Curve:
r"""Produces arc or full circle curve
Produces arc using the following parametric... | /scikit_curve-0.1.0-py3-none-any.whl/curve/curves.py | 0.946868 | 0.690771 | curves.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
import numpy as np
import pandas as pd
from .extraction import activity_power_profile
from .io import bikeread
from .utils import validate_filenames
class Rider(object):
"""User interface for a rider.
... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/base.py | 0.886942 | 0.483587 | base.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
from __future__ import division
from collections import Iterable
import pandas as pd
from ..exceptions import MissingDataError
def acceleration(activity, periods=5, append=True):
"""Compute the accelerat... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/extraction/gradient.py | 0.95638 | 0.836821 | gradient.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
import os
from collections import defaultdict
import pandas as pd
import numpy as np
import six
from fitparse import FitFile
# 'timestamp' will be consider as the index of the DataFrame later on
FIELDS_DATA = ... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/io/fit.py | 0.808899 | 0.517449 | fit.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
import numpy as np
from .fit import load_power_from_fit
DROP_OPTIONS = ('columns', 'rows', 'both')
def bikeread(filename, drop_nan=None):
"""Read power data file.
Read more in the :ref:`User Guide <r... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/io/base.py | 0.875348 | 0.572424 | base.py | pypi |
from os import listdir
from os.path import dirname, join
__all__ = ['load_fit',
'load_rider']
def load_fit(returned_type='list_file', set_data='normal'):
"""Return path to some FIT toy data.
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
returned_type : str, op... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/datasets/__init__.py | 0.783533 | 0.349921 | __init__.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
from __future__ import division
import numpy as np
TS_SCALE_GRAPPE = dict([('I1', 2.), ('I2', 2.5), ('I3', 3.),
('I4', 3.5), ('I5', 4.5), ('I6', 7.),
('I7', 11.)])... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/metrics/activity.py | 0.943906 | 0.683014 | activity.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
from __future__ import division
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
SAMPLING_WKO = pd.TimedeltaIndex(
['00:00:01', '00:00:05', '00:00:30', '00:01:00', ... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/metrics/power_profile.py | 0.93638 | 0.803791 | power_profile.py | pypi |
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Cedric Lemaitre
# License: BSD 3 clause
from __future__ import division
import numpy as np
from scipy import constants
from ..extraction import gradient_elevation
from ..extraction import acceleration
def strava_power_model(activity, cyclist_weight... | /scikit_cycling-0.1.3-cp35-cp35m-win32.whl/skcycling/model/power.py | 0.940939 | 0.582491 | power.py | pypi |
===============================
SciKit Data
===============================
.. image:: https://img.shields.io/pypi/v/scikit-data.svg
:target: https://pypi.python.org/pypi/scikit-data
.. image:: https://img.shields.io/travis/OpenDataScienceLab/skdata.svg
:target: https://travis-ci.org/OpenDataScienceL... | /scikit-data-0.1.3.tar.gz/scikit-data-0.1.3/README.rst | 0.946609 | 0.878991 | README.rst | pypi |
from functools import reduce
# local
from .cleaning import *
import json
import numpy as np
import pandas as pd
class StepSkData:
parent = None
def __init__(self, parent: 'SkDataSet'):
"""
:param parent:
"""
self.parent = parent
def compute(
self, start: int = N... | /scikit-data-0.1.3.tar.gz/scikit-data-0.1.3/skdata/steps.py | 0.547464 | 0.368406 | steps.py | pypi |
<p align="left">
<img alt="Scikit Data Access" src="https://github.com/MITHaystack/scikit-dataaccess/raw/master/skdaccess/docs/images/skdaccess_logo360x100.png"/>
</p>
- Import scientific data from various sources through one easy Python API.
- Use iterator patterns for each data source (configurable data generators... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/README.md | 0.613005 | 0.765987 | README.md | pypi |
# Skdaccess imports
from skdaccess.framework.data_class import DataFetcherCache, TableWrapper
from skdaccess.framework.param_class import *
# Standard library imports
from collections import OrderedDict
import re
# 3rd part imports
import pandas as pd
class DataFetcher(DataFetcherCache):
'''
Data Fetcher ... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/astro/voyager/data_fetcher.py | 0.680348 | 0.420243 | data_fetcher.py | pypi |
# mithagi required Base,Utils imports
from skdaccess.framework.data_class import DataFetcherCache, TableWrapper
from skdaccess.utilities.tess_utils import parseTessData
# Standard library imports
from collections import OrderedDict
# Third pary imports
from astropy.io import fits
from astropy.table import Table
impo... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/astro/tess/generic/cache.py | 0.636805 | 0.297285 | cache.py | pypi |
# """@package Kepler
# Provides classes for accessing Kepler data.
# """
# mithagi required Base,Utils imports
from skdaccess.framework.data_class import DataFetcherCache, TableWrapper
from skdaccess.utilities.file_util import openPandasHDFStoreLocking
# Standard library imports
import re
import glob
import os
from ... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/astro/kepler/data_fetcher.py | 0.602529 | 0.340266 | data_fetcher.py | pypi |
The MIT License (MIT)<br>
Copyright (c) 2016,2017 Massachusetts Institute of Technology<br>
Authors: Justin Li, Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person obtaini... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/examples/Demo_GLDAS.ipynb | 0.514644 | 0.564459 | Demo_GLDAS.ipynb | pypi |
The MIT License (MIT)<br>
Copyright (c) 2016,2017 Massachusetts Institute of Technology<br>
Authors: Justin Li, Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person obtaini... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/examples/Demo_PBO.ipynb | 0.818047 | 0.566798 | Demo_PBO.ipynb | pypi |
The MIT License (MIT)<br>
Copyright (c) 2018 Massachusetts Institute of Technology<br>
Authors: Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person obtaining a copy<br>
of... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/examples/Demo_TESS_Simulated_Data.ipynb | 0.403097 | 0.647534 | Demo_TESS_Simulated_Data.ipynb | pypi |
The MIT License (MIT)<br>
Copyright (c) 2017 Massachusetts Institute of Technology<br>
Author: Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person obtaining a copy<br>
of ... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/examples/Demo_Mahali_Temperature.ipynb | 0.518546 | 0.658143 | Demo_Mahali_Temperature.ipynb | pypi |
The MIT License (MIT)<br>
Copyright (c) 2016, 2017, 2018 Massachusetts Institute of Technology<br>
Authors: Justin Li, Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI: Pankratius)<br>
Permission is hereby granted, free of charge, to any person ... | /scikit-dataaccess-1.9.17.tar.gz/scikit-dataaccess-1.9.17/skdaccess/examples/Demo_GRACE_Mascon.ipynb | 0.707506 | 0.567607 | Demo_GRACE_Mascon.ipynb | pypi |
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