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train | SGMLBaseValidator.save | Save validator object to pickle. | ml_tools/eolearn/ml_tools/validator.py | def save(self, filename):
"""
Save validator object to pickle.
"""
with open(filename, 'wb') as output:
pickle.dump(self, output, protocol=pickle.HIGHEST_PROTOCOL) | def save(self, filename):
"""
Save validator object to pickle.
"""
with open(filename, 'wb') as output:
pickle.dump(self, output, protocol=pickle.HIGHEST_PROTOCOL) | [
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train | SGMLBaseValidator.pandas_df | Returns pandas DataFrame containing pixel counts for all truth classes,
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EODataSet.
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EODataSet.
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N = self.n_validation_sets rows
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train | SGMLBaseValidator.confusion_matrix | Returns the normalised confusion matrix | ml_tools/eolearn/ml_tools/validator.py | def confusion_matrix(self):
"""
Returns the normalised confusion matrix
"""
confusion_matrix = self.pixel_classification_sum.astype(np.float)
confusion_matrix = np.divide(confusion_matrix.T, self.pixel_truth_sum.T).T
return confusion_matrix * 100.0 | def confusion_matrix(self):
"""
Returns the normalised confusion matrix
"""
confusion_matrix = self.pixel_classification_sum.astype(np.float)
confusion_matrix = np.divide(confusion_matrix.T, self.pixel_truth_sum.T).T
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train | SGMLBaseValidator.plot_confusion_matrix | Plots the confusion matrix. | ml_tools/eolearn/ml_tools/validator.py | def plot_confusion_matrix(self, normalised=True):
"""
Plots the confusion matrix.
"""
conf_matrix = self.confusion_matrix()
if normalised:
sns.heatmap(conf_matrix,
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"""
Plots the confusion matrix.
"""
conf_matrix = self.confusion_matrix()
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sns.heatmap(conf_matrix,
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train | SGMLBaseValidator.summary | Prints out the summary of validation for giving scoring function. | ml_tools/eolearn/ml_tools/validator.py | def summary(self, scoring):
"""
Prints out the summary of validation for giving scoring function.
"""
if scoring == 'class_confusion':
print('*' * 50)
print(' Confusion Matrix ')
print('x-axis: ' + ' | '.join(list(self.class_dictionary.keys())))
... | def summary(self, scoring):
"""
Prints out the summary of validation for giving scoring function.
"""
if scoring == 'class_confusion':
print('*' * 50)
print(' Confusion Matrix ')
print('x-axis: ' + ' | '.join(list(self.class_dictionary.keys())))
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train | SimpleFilterTask.execute | :param eopatch: Input EOPatch.
:type eopatch: EOPatch
:return: Transformed eo patch
:rtype: EOPatch | features/eolearn/features/feature_manipulation.py | def execute(self, eopatch):
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:rtype: EOPatch
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feature_type, feature_name = next(self.feature(eopatch))
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train | HOGTask.execute | Execute computation of HoG features on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
:return: EOPatch instance with new keys holding the HoG features and HoG image for visualisation.
:rtype: eolearn.core.EOPatch | features/eolearn/features/hog.py | def execute(self, eopatch):
""" Execute computation of HoG features on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
:return: EOPatch instance with new keys holding the HoG features and HoG image for visualisation.
:rtype: eolear... | def execute(self, eopatch):
""" Execute computation of HoG features on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
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train | LocalBinaryPatternTask.execute | Execute computation of local binary patterns on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
:return: EOPatch instance with new key holding the LBP image.
:rtype: eolearn.core.EOPatch | features/eolearn/features/local_binary_pattern.py | def execute(self, eopatch):
""" Execute computation of local binary patterns on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
:return: EOPatch instance with new key holding the LBP image.
:rtype: eolearn.core.EOPatch
"""
... | def execute(self, eopatch):
""" Execute computation of local binary patterns on input eopatch
:param eopatch: Input eopatch
:type eopatch: eolearn.core.EOPatch
:return: EOPatch instance with new key holding the LBP image.
:rtype: eolearn.core.EOPatch
"""
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train | ransac | Fit model parameters to data using the RANSAC algorithm
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:param npts: A set of observed data points
:param model: A model that can be fitted to data points
:param n: The minimum... | coregistration/eolearn/coregistration/coregistration_utilities.py | def ransac(npts, model, n, k, t, d):
""" Fit model parameters to data using the RANSAC algorithm
This implementation is written from pseudo-code found at
http://en.wikipedia.org/w/index.php?title=RANSAC&oldid=116358182
:param npts: A set of observed data points
:param model: A model that can be fi... | def ransac(npts, model, n, k, t, d):
""" Fit model parameters to data using the RANSAC algorithm
This implementation is written from pseudo-code found at
http://en.wikipedia.org/w/index.php?title=RANSAC&oldid=116358182
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train | random_partition | return n random rows of data (and also the other len(data)-n rows) | coregistration/eolearn/coregistration/coregistration_utilities.py | def random_partition(n, n_data):
"""return n random rows of data (and also the other len(data)-n rows)"""
all_idxs = np.arange(n_data)
np.random.shuffle(all_idxs)
idxs1 = all_idxs[:n]
idxs2 = all_idxs[n:]
return idxs1, idxs2 | def random_partition(n, n_data):
"""return n random rows of data (and also the other len(data)-n rows)"""
all_idxs = np.arange(n_data)
np.random.shuffle(all_idxs)
idxs1 = all_idxs[:n]
idxs2 = all_idxs[n:]
return idxs1, idxs2 | [
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train | EstimateEulerTransformModel.estimate_rigid_transformation | Estimate rigid transformation given a set of indices
:param idx: Array of indices used to estimate the transformation
:return: Estimated transformation matrix | coregistration/eolearn/coregistration/coregistration_utilities.py | def estimate_rigid_transformation(self, idx):
""" Estimate rigid transformation given a set of indices
:param idx: Array of indices used to estimate the transformation
:return: Estimated transformation matrix
"""
# Look at points of given indices only
src_pts = self.src_... | def estimate_rigid_transformation(self, idx):
""" Estimate rigid transformation given a set of indices
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:return: Estimated transformation matrix
"""
# Look at points of given indices only
src_pts = self.src_... | [
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train | EstimateEulerTransformModel.score | Estimate the registration error of estimated transformation matrix
:param idx: List of points used to estimate the transformation
:param warp_matrix: Matrix estimating Euler trasnformation
:return: Square root of Target Registration Error | coregistration/eolearn/coregistration/coregistration_utilities.py | def score(self, idx, warp_matrix):
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:param idx: List of points used to estimate the transformation
:param warp_matrix: Matrix estimating Euler trasnformation
:return: Square root of Target Registration Error
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:param warp_matrix: Matrix estimating Euler trasnformation
:return: Square root of Target Registration Error
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train | rolling_window | Create a view of `array` which for every point gives the n-dimensional
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`array` or after the corresponding original dimension.
Parameters
----------
array : array_like
Array to which the rolling window is applied.
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neighbourhood of size window. New dimensions are added at the end of
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train | RegistrationTask.execute | Method that estimates registrations and warps EOPatch objects | coregistration/eolearn/coregistration/coregistration.py | def execute(self, eopatch):
""" Method that estimates registrations and warps EOPatch objects
"""
self.check_params()
self.get_params()
new_eopatch = copy.deepcopy(eopatch)
f_type, f_name = next(self.registration_feature(eopatch))
sliced_data = copy.deepcopy(eop... | def execute(self, eopatch):
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self.check_params()
self.get_params()
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train | RegistrationTask.warp | Function to warp input image given an estimated 2D linear transformation
:param warp_matrix: Linear 2x3 matrix to use to linearly warp the input images
:type warp_matrix: ndarray
:param img: Image to be warped with estimated transformation
:type img: ndarray
:param iflag: Interp... | coregistration/eolearn/coregistration/coregistration.py | def warp(self, warp_matrix, img, iflag=cv2.INTER_NEAREST):
""" Function to warp input image given an estimated 2D linear transformation
:param warp_matrix: Linear 2x3 matrix to use to linearly warp the input images
:type warp_matrix: ndarray
:param img: Image to be warped with estimated... | def warp(self, warp_matrix, img, iflag=cv2.INTER_NEAREST):
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train | RegistrationTask.is_registration_suspicious | Static method that check if estimated linear transformation could be unplausible
This function checks whether the norm of the estimated translation or the rotation angle exceed predefined
values. For the translation, a maximum translation radius of 20 pixels is flagged, while larger rotations than
... | coregistration/eolearn/coregistration/coregistration.py | def is_registration_suspicious(warp_matrix):
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train | ThunderRegistration.register | Implementation of pair-wise registration using thunder-registration
For more information on the model estimation, refer to https://github.com/thunder-project/thunder-registration
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estim... | coregistration/eolearn/coregistration/coregistration.py | def register(self, src, trg, trg_mask=None, src_mask=None):
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For more information on the model estimation, refer to https://github.com/thunder-project/thunder-registration
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train | ECCRegistration.register | Implementation of pair-wise registration and warping using Enhanced Correlation Coefficient
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train | PointBasedRegistration.register | Implementation of pair-wise registration and warping using point-based matching
This function estimates a number of transforms (Euler, PartialAffine and Homography) using point-based matching.
Features descriptor are first extracted from the pair of images using either SIFT or SURF descriptors. A
... | coregistration/eolearn/coregistration/coregistration.py | def register(self, src, trg, trg_mask=None, src_mask=None):
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train | PointBasedRegistration.rescale_image | Normalise and scale image in 0-255 range | coregistration/eolearn/coregistration/coregistration.py | def rescale_image(image):
""" Normalise and scale image in 0-255 range """
s2_min_value, s2_max_value = 0, 1
out_min_value, out_max_value = 0, 255
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image[image > s2_max_value] = s2_max_value
image[image < s2_min_value] = s2_min_value
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train | Mask2Label.transform | Parameters
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train | VectorToRaster._get_submap | Returns a new geopandas dataframe with same structure as original one (columns) except that
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:type eopatch: EOPatch
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:type eopatch: EOPatch
:return: New EOPatch
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train | VectorToRaster.execute | Execute function which adds new vector layer to the EOPatch
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:type eopatch: EOPatch
:return: New EOPatch with added vector layer
:rtype: EOPatch | geometry/eolearn/geometry/utilities.py | def execute(self, eopatch):
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train | RasterToVector._vectorize_single_raster | Vectorizes a data slice of a single time component
:param raster: Numpy array or shape (height, width, channels)
:type raster: numpy.ndarray
:param data_transform: Object holding a transform vector (i.e. geographical location vector) of the raster
:type data_transform: affine.Affine
... | geometry/eolearn/geometry/utilities.py | def _vectorize_single_raster(self, raster, data_transform, crs, timestamp=None):
""" Vectorizes a data slice of a single time component
:param raster: Numpy array or shape (height, width, channels)
:type raster: numpy.ndarray
:param data_transform: Object holding a transform vector (i.e... | def _vectorize_single_raster(self, raster, data_transform, crs, timestamp=None):
""" Vectorizes a data slice of a single time component
:param raster: Numpy array or shape (height, width, channels)
:type raster: numpy.ndarray
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train | RasterToVector.execute | Execute function which adds new vector layer to the EOPatch
:param eopatch: input EOPatch
:type eopatch: EOPatch
:return: New EOPatch with added vector layer
:rtype: EOPatch | geometry/eolearn/geometry/utilities.py | def execute(self, eopatch):
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:return: New EOPatch with added vector layer
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:type eopatch: EOPatch
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train | PointSampler.area | Returns the area of the selected polygon if index is provided or of all polygons if it's not. | geometry/eolearn/geometry/sampling.py | def area(self, cc_index=None):
"""
Returns the area of the selected polygon if index is provided or of all polygons if it's not.
"""
if cc_index is not None:
return self.areas[cc_index]
return np.sum(self.areas) | def area(self, cc_index=None):
"""
Returns the area of the selected polygon if index is provided or of all polygons if it's not.
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if cc_index is not None:
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train | PointSampler.sample | Sample n points from the provided raster mask. The number of points belonging
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TODO: If polygon has holes the number of sampled points will be less than nsamples
:param nsamples: ... | geometry/eolearn/geometry/sampling.py | def sample(self, nsamples=1, weighted=True):
"""
Sample n points from the provided raster mask. The number of points belonging
to each class is proportional to the area this class covers in the raster mask,
if weighted is set to True.
TODO: If polygon has holes the number of sam... | def sample(self, nsamples=1, weighted=True):
"""
Sample n points from the provided raster mask. The number of points belonging
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train | PointSampler.sample_cc | Returns a random polygon of any class. The probability of each polygon to be sampled
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train | PointSampler.sample_within_cc | Returns randomly sampled points from a polygon.
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train | PointSampler.contains | Tests whether point lies within the polygon | geometry/eolearn/geometry/sampling.py | def contains(polygon, point):
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Tests whether point lies within the polygon
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train | PointSampler.random_coords | Selects a random point in interior of a rectangle
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:type bounds: tuple(float)
:return: Random point from interior of rectangle
:rtype: tuple of x and y coordinates | geometry/eolearn/geometry/sampling.py | def random_coords(bounds):
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:type bounds: tuple(float)
:return: Random point from interior of rectangle
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:type bounds: tuple(float)
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train | PointSampler.random_point_triangle | Selects a random point in interior of a triangle | geometry/eolearn/geometry/sampling.py | def random_point_triangle(triangle, use_int_coords=True):
"""
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train | PointRasterSampler._get_unknown_value | Finds the smallest integer value >=0 that is not in `labels`
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:rtype: int | geometry/eolearn/geometry/sampling.py | def _get_unknown_value(self):
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train | PointRasterSampler._binary_sample | Sample `nsamples_per_label` points from the binary mask corresponding to `label`
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train | PointRasterSampler.sample | Sample `nsamples` points form raster
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:param n_samples: Number of points to sample in total
:type n_samples: uint32
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:param raster: Input 2D or single-channel 3D label image
:type raster: uint8 numpy array
:param n_samples: Number of points to sample in total
:type n_samples: uint32
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train | PointSamplingTask.execute | Execute random spatial sampling of time-series stored in the input eopatch
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train | SaveToDisk.execute | Saves the EOPatch to disk: `folder/eopatch_folder`.
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train | LoadFromDisk.execute | Loads the EOPatch from disk: `folder/eopatch_folder`.
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:return: EOPatch loaded from disk
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train | AddFeature.execute | Returns the EOPatch with added features.
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:param data: data to be added to the feature
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:return: input EOPatch with the specified feature
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train | RemoveFeature.execute | Returns the EOPatch with removed features.
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:return: input EOPatch without the specified feature
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train | RenameFeature.execute | Returns the EOPatch with renamed features.
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:return: input EOPatch with the renamed features
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train | BaseCompositing._numpy_index_by_percentile | Calculate percentile of numpy stack and return the index of the chosen pixel.
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train | BaseCompositing._geoville_index_by_percentile | Calculate percentile of numpy stack and return the index of the chosen pixel. | features/eolearn/features/radiometric_normalization.py | def _geoville_index_by_percentile(self, data, percentile):
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train | BaseCompositing._get_indices | Compute indices along temporal dimension corresponding to the sought percentile
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train | MaxNDVICompositing._get_reference_band | Extract the NDVI band from time-series
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train | MaxNDWICompositing._get_reference_band | Extract the NDWI band from time-series
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train | HistogramMatching.execute | Perform histogram matching of the time-series with respect to a reference scene
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train | _FeatureDict._parse_feature_value | Checks if value fits the feature type. If not it tries to fix it or raise an error
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train | _FileLoader.load | Method which loads data from the file | core/eolearn/core/eodata.py | def load(self):
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train | _FileSaver.get_file_path | Creates a filename with file path | core/eolearn/core/eodata.py | def get_file_path(self, path):
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train | _FileSaver._get_filename_path | Helper function for creating filename without file extension | core/eolearn/core/eodata.py | def _get_filename_path(self, path):
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train | InterpolationTask._mask_feature_data | Masks values of data feature with a given mask of given mask type. The masking is done by assigning
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:param feature_data: Data array which will be masked
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train | InterpolationTask._get_start_end_nans | Find NaN values in data that either start or end the time-series
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:param data: Ar... | features/eolearn/features/interpolation.py | def _get_start_end_nans(data):
""" Find NaN values in data that either start or end the time-series
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train | InterpolationTask._get_unique_times | Replace duplicate acquisitions which have same values on the chosen time scale with their average.
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:param data: Array in a shape of t x nobs, where nobs = h x w x n
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train | InterpolationTask._copy_old_features | Copy features from old EOPatch
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:param new_eopatch: New EOPatch container where the old features will be copied to
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""" Copy features from old EOPatch
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train | InterpolationTask.interpolate_data | Interpolates data feature
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:param times: Array of reference times relative to the first timestamp
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train | InterpolationTask.get_interpolation_function | Initializes interpolation model
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:param series: One dimensional array of time series
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:param series: One dimensional array of time series
:type series: numpy.array
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:type times: numpy.array
:param series: One dimensional array of time series
:type series: numpy.array
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train | InterpolationTask.get_resampled_timestamp | Takes a list of timestamps and generates new list of timestamps according to ``resample_range``
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:type timestamp: list(datetime.datetime)
:return: new list of timestamps
:rtype: list(datetime.datetime) | features/eolearn/features/interpolation.py | def get_resampled_timestamp(self, timestamp):
""" Takes a list of timestamps and generates new list of timestamps according to ``resample_range``
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""" Takes a list of timestamps and generates new list of timestamps according to ``resample_range``
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train | InterpolationTask.execute | Execute method that processes EOPatch and returns EOPatch | features/eolearn/features/interpolation.py | def execute(self, eopatch):
""" Execute method that processes EOPatch and returns EOPatch
"""
# pylint: disable=too-many-locals
feature_type, feature_name, new_feature_name = next(self.feature(eopatch))
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:param times: Array of reference times in second relative to the first timestamp
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train | ResamplingTask.get_interpolation_function | Initializes interpolation model
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:param data: One dimensional array of time series
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train | get_text | return inner text in pyquery element | howdoi/howdoi.py | def get_text(element):
''' return inner text in pyquery element '''
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try:
return element.text(squash_space=False)
except TypeError:
return element.text() | def get_text(element):
''' return inner text in pyquery element '''
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except TypeError:
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train | download_file | Simple wrapper around urlretrieve that uses tqdm to display a progress
bar of download progress | implicit/datasets/_download.py | def download_file(url, local_filename):
""" Simple wrapper around urlretrieve that uses tqdm to display a progress
bar of download progress """
local_filename = os.path.abspath(local_filename)
path = os.path.dirname(local_filename)
if not os.path.isdir(path):
os.makedirs(path)
with tqdm... | def download_file(url, local_filename):
""" Simple wrapper around urlretrieve that uses tqdm to display a progress
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local_filename = os.path.abspath(local_filename)
path = os.path.dirname(local_filename)
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train | extract_gcc_binaries | Try to find GCC on OSX for OpenMP support. | setup.py | def extract_gcc_binaries():
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patterns = ['/opt/local/bin/g++-mp-[0-9].[0-9]',
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"""Try to find GCC on OSX for OpenMP support."""
patterns = ['/opt/local/bin/g++-mp-[0-9].[0-9]',
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train | set_gcc | Try to use GCC on OSX for OpenMP support. | setup.py | def set_gcc():
"""Try to use GCC on OSX for OpenMP support."""
# For macports and homebrew
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gcc = extract_gcc_binaries()
if gcc is not None:
os.environ["CC"] = gcc
os.environ["CXX"] = gcc
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global u... | def set_gcc():
"""Try to use GCC on OSX for OpenMP support."""
# For macports and homebrew
if 'darwin' in platform.platform().lower():
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os.environ["CC"] = gcc
os.environ["CXX"] = gcc
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train | nonzeros | returns the non zeroes of a row in csr_matrix | implicit/utils.py | def nonzeros(m, row):
""" returns the non zeroes of a row in csr_matrix """
for index in range(m.indptr[row], m.indptr[row+1]):
yield m.indices[index], m.data[index] | def nonzeros(m, row):
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train | _read_dataframe | Reads the original dataset PSV as a pandas dataframe | implicit/datasets/sketchfab.py | def _read_dataframe(filename):
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train | get_lastfm | Returns the lastfm360k dataset, downloading locally if necessary.
Returns a tuple of (artistids, userids, plays) where plays is a CSR matrix | implicit/datasets/lastfm.py | def get_lastfm():
""" Returns the lastfm360k dataset, downloading locally if necessary.
Returns a tuple of (artistids, userids, plays) where plays is a CSR matrix """
filename = os.path.join(_download.LOCAL_CACHE_DIR, "lastfm_360k.hdf5")
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train | alternating_least_squares | factorizes the matrix Cui using an implicit alternating least squares
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train | least_squares | For each user in Cui, calculate factors Xu for them
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train | AlternatingLeastSquares._fit_gpu | specialized training on the gpu. copies inputs to/from cuda device | implicit/als.py | def _fit_gpu(self, Ciu_host, Cui_host, show_progress=True):
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train | AlternatingLeastSquares.explain | Provides explanations for why the item is liked by the user.
Parameters
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userid : int
The userid to explain recommendations for
user_items : csr_matrix
Sparse matrix containing the liked items for the user
itemid : int
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""" Provides explanations for why the item is liked by the user.
Parameters
---------
userid : int
The userid to explain recommendations for
user_items : csr_matrix
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userid : int
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train | augment_inner_product_matrix | This function transforms a factor matrix such that an angular nearest neighbours search
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train | convert_sparse_to_dataframe | Converts a scipy sparse matrix to a spark dataframe | benchmarks/benchmark_spark.py | def convert_sparse_to_dataframe(spark, context, sparse_matrix):
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m = sparse_matrix.tocoo()
data = context.parallelize(numpy.array([m.row, m.col, m.data]).T,
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m = sparse_matrix.tocoo()
data = context.parallelize(numpy.array([m.row, m.col, m.data]).T,
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train | generate_dataset | Generates a hdf5 datasetfile from the raw datafiles:
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https://labrosa.ee.columbia.edu/millionsong/tasteprofile#getting
And the 'Summary File of the whole dataset' from here
https://labrosa.ee.columbia.edu/millionsong/pages/getting-dataset
... | implicit/datasets/million_song_dataset.py | def generate_dataset(triplets_filename, summary_filename="msd_summary_file.h5",
outputfilename="msd_taste_profile.hdf5"):
""" Generates a hdf5 datasetfile from the raw datafiles:
You will need to download the train_triplets from here:
https://labrosa.ee.columbia.edu/millionsong/tas... | def generate_dataset(triplets_filename, summary_filename="msd_summary_file.h5",
outputfilename="msd_taste_profile.hdf5"):
""" Generates a hdf5 datasetfile from the raw datafiles:
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train | _read_triplets_dataframe | Reads the original dataset TSV as a pandas dataframe | implicit/datasets/million_song_dataset.py | def _read_triplets_dataframe(filename):
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# delay importing this to avoid another dependency
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# read in triples of user/artist/playcount from the input dataset
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train | _join_summary_file | Gets the trackinfo array by joining taste profile to the track summary file | implicit/datasets/million_song_dataset.py | def _join_summary_file(data, summary_filename="msd_summary_file.h5"):
""" Gets the trackinfo array by joining taste profile to the track summary file """
msd = h5py.File(summary_filename)
# create a lookup table of trackid -> position
track_lookup = dict((t.encode("utf8"), i) for i, t in enumerate(data... | def _join_summary_file(data, summary_filename="msd_summary_file.h5"):
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msd = h5py.File(summary_filename)
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train | tfidf_weight | Weights a Sparse Matrix by TF-IDF Weighted | implicit/nearest_neighbours.py | def tfidf_weight(X):
""" Weights a Sparse Matrix by TF-IDF Weighted """
X = coo_matrix(X)
# calculate IDF
N = float(X.shape[0])
idf = log(N) - log1p(bincount(X.col))
# apply TF-IDF adjustment
X.data = sqrt(X.data) * idf[X.col]
return X | def tfidf_weight(X):
""" Weights a Sparse Matrix by TF-IDF Weighted """
X = coo_matrix(X)
# calculate IDF
N = float(X.shape[0])
idf = log(N) - log1p(bincount(X.col))
# apply TF-IDF adjustment
X.data = sqrt(X.data) * idf[X.col]
return X | [
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train | normalize | equivalent to scipy.preprocessing.normalize on sparse matrices
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train | bm25_weight | Weighs each row of a sparse matrix X by BM25 weighting | implicit/nearest_neighbours.py | def bm25_weight(X, K1=100, B=0.8):
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# calculate idf per term (user)
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train | ItemItemRecommender.fit | Computes and stores the similarity matrix | implicit/nearest_neighbours.py | def fit(self, weighted, show_progress=True):
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train | ItemItemRecommender.recommend | returns the best N recommendations for a user given its id | implicit/nearest_neighbours.py | def recommend(self, userid, user_items,
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""" returns the best N recommendations for a user given its id"""
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train | ItemItemRecommender.rank_items | Rank given items for a user and returns sorted item list | implicit/nearest_neighbours.py | def rank_items(self, userid, user_items, selected_items, recalculate_user=False):
""" Rank given items for a user and returns sorted item list """
# check if selected_items contains itemids that are not in the model(user_items)
if max(selected_items) >= user_items.shape[1] or min(selected_items)... | def rank_items(self, userid, user_items, selected_items, recalculate_user=False):
""" Rank given items for a user and returns sorted item list """
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train | ItemItemRecommender.similar_items | Returns a list of the most similar other items | implicit/nearest_neighbours.py | def similar_items(self, itemid, N=10):
""" Returns a list of the most similar other items """
if itemid >= self.similarity.shape[0]:
return []
return sorted(list(nonzeros(self.similarity, itemid)), key=lambda x: -x[1])[:N] | def similar_items(self, itemid, N=10):
""" Returns a list of the most similar other items """
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train | get_reddit | Returns the reddit dataset, downloading locally if necessary.
This dataset was released here:
https://www.reddit.com/r/redditdev/comments/dtg4j/want_to_help_reddit_build_a_recommender_a_public/
and contains 23M up/down votes from 44K users on 3.4M links.
Returns a CSR matrix of (item, user, rating | implicit/datasets/reddit.py | def get_reddit():
""" Returns the reddit dataset, downloading locally if necessary.
This dataset was released here:
https://www.reddit.com/r/redditdev/comments/dtg4j/want_to_help_reddit_build_a_recommender_a_public/
and contains 23M up/down votes from 44K users on 3.4M links.
Returns a CSR matrix ... | def get_reddit():
""" Returns the reddit dataset, downloading locally if necessary.
This dataset was released here:
https://www.reddit.com/r/redditdev/comments/dtg4j/want_to_help_reddit_build_a_recommender_a_public/
and contains 23M up/down votes from 44K users on 3.4M links.
Returns a CSR matrix ... | [
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train | _read_dataframe | Reads the original dataset TSV as a pandas dataframe | implicit/datasets/reddit.py | def _read_dataframe(filename):
""" Reads the original dataset TSV as a pandas dataframe """
# delay importing this to avoid another dependency
import pandas
# read in triples of user/artist/playcount from the input dataset
# get a model based off the input params
start = time.time()
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""" Reads the original dataset TSV as a pandas dataframe """
# delay importing this to avoid another dependency
import pandas
# read in triples of user/artist/playcount from the input dataset
# get a model based off the input params
start = time.time()
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train | get_movielens | Gets movielens datasets
Parameters
---------
variant : string
Which version of the movielens dataset to download. Should be one of '20m', '10m',
'1m' or '100k'.
Returns
-------
movies : ndarray
An array of the movie titles.
ratings : csr_matrix
A sparse matr... | implicit/datasets/movielens.py | def get_movielens(variant="20m"):
""" Gets movielens datasets
Parameters
---------
variant : string
Which version of the movielens dataset to download. Should be one of '20m', '10m',
'1m' or '100k'.
Returns
-------
movies : ndarray
An array of the movie titles.
... | def get_movielens(variant="20m"):
""" Gets movielens datasets
Parameters
---------
variant : string
Which version of the movielens dataset to download. Should be one of '20m', '10m',
'1m' or '100k'.
Returns
-------
movies : ndarray
An array of the movie titles.
... | [
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train | generate_dataset | Generates a hdf5 movielens datasetfile from the raw datafiles found at:
https://grouplens.org/datasets/movielens/20m/
You shouldn't have to run this yourself, and can instead just download the
output using the 'get_movielens' funciton./ | implicit/datasets/movielens.py | def generate_dataset(path, variant='20m', outputpath="."):
""" Generates a hdf5 movielens datasetfile from the raw datafiles found at:
https://grouplens.org/datasets/movielens/20m/
You shouldn't have to run this yourself, and can instead just download the
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... | def generate_dataset(path, variant='20m', outputpath="."):
""" Generates a hdf5 movielens datasetfile from the raw datafiles found at:
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You shouldn't have to run this yourself, and can instead just download the
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train | _read_dataframes_20M | reads in the movielens 20M | implicit/datasets/movielens.py | def _read_dataframes_20M(path):
""" reads in the movielens 20M"""
import pandas
ratings = pandas.read_csv(os.path.join(path, "ratings.csv"))
movies = pandas.read_csv(os.path.join(path, "movies.csv"))
return ratings, movies | def _read_dataframes_20M(path):
""" reads in the movielens 20M"""
import pandas
ratings = pandas.read_csv(os.path.join(path, "ratings.csv"))
movies = pandas.read_csv(os.path.join(path, "movies.csv"))
return ratings, movies | [
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train | _read_dataframes_100k | reads in the movielens 100k dataset | implicit/datasets/movielens.py | def _read_dataframes_100k(path):
""" reads in the movielens 100k dataset"""
import pandas
ratings = pandas.read_table(os.path.join(path, "u.data"),
names=['userId', 'movieId', 'rating', 'timestamp'])
movies = pandas.read_csv(os.path.join(path, "u.item"),
... | def _read_dataframes_100k(path):
""" reads in the movielens 100k dataset"""
import pandas
ratings = pandas.read_table(os.path.join(path, "u.data"),
names=['userId', 'movieId', 'rating', 'timestamp'])
movies = pandas.read_csv(os.path.join(path, "u.item"),
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train | RecommenderBase.recommend | Recommends items for a user
Calculates the N best recommendations for a user, and returns a list of itemids, score.
Parameters
----------
userid : int
The userid to calculate recommendations for
user_items : csr_matrix
A sparse matrix of shape (number_us... | implicit/recommender_base.py | def recommend(self, userid, user_items,
N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
"""
Recommends items for a user
Calculates the N best recommendations for a user, and returns a list of itemids, score.
Parameters
------... | def recommend(self, userid, user_items,
N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False):
"""
Recommends items for a user
Calculates the N best recommendations for a user, and returns a list of itemids, score.
Parameters
------... | [
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train | find_in_path | Find a file in a search path | cuda_setup.py | def find_in_path(name, path):
"Find a file in a search path"
# adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
for dir in path.split(os.pathsep):
binpath = os.path.join(dir, name)
if os.path.exists(binpath):
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... | def find_in_path(name, path):
"Find a file in a search path"
# adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
for dir in path.split(os.pathsep):
binpath = os.path.join(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
... | [
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... | 6b16c50d1d514a814f2e5b8cf2a829ff23dbba63 |
train | locate_cuda | Locate the CUDA environment on the system
If a valid cuda installation is found this returns a dict with keys 'home', 'nvcc', 'include',
and 'lib64' and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything is based on finding
'nvc... | cuda_setup.py | def locate_cuda():
"""Locate the CUDA environment on the system
If a valid cuda installation is found this returns a dict with keys 'home', 'nvcc', 'include',
and 'lib64' and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything is... | def locate_cuda():
"""Locate the CUDA environment on the system
If a valid cuda installation is found this returns a dict with keys 'home', 'nvcc', 'include',
and 'lib64' and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything is... | [
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"the",
"system"
] | benfred/implicit | python | https://github.com/benfred/implicit/blob/6b16c50d1d514a814f2e5b8cf2a829ff23dbba63/cuda_setup.py#L21-L74 | [
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... | 6b16c50d1d514a814f2e5b8cf2a829ff23dbba63 |
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