Python scipy.spatial.distance.canberra() Examples
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code examples of scipy.spatial.distance.canberra().
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Example #1
| Source File: make_handcrafted_33_features.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #2
| Source File: train_predict_trees_batch2.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #3
| Source File: train_predict_trees_batch1.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #4
| Source File: train_predict_trees_batch3.py From wsdm19cup with MIT License | 5 votes |
|
def __calc_distances__(self, v1s, v2s, is_sparse=True):
if is_sparse:
dcosine = np.array([cosine(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x.toarray(), y.toarray(), 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x.toarray(), y.toarray()) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x.toarray().ravel()) for x in v1s]
dskew_q2 = [skew(x.toarray().ravel()) for x in v2s]
dkur_q1 = [kurtosis(x.toarray().ravel()) for x in v1s]
dkur_q2 = [kurtosis(x.toarray().ravel()) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
else:
dcosine = np.array([cosine(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcityblock = np.array([cityblock(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dcanberra = np.array([canberra(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
deuclidean = np.array([euclidean(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dminkowski = np.array([minkowski(x, y, 3) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dbraycurtis = np.array([braycurtis(x, y) for (x, y) in zip(v1s, v2s)]).reshape((-1,1))
dskew_q1 = [skew(x) for x in v1s]
dskew_q2 = [skew(x) for x in v2s]
dkur_q1 = [kurtosis(x) for x in v1s]
dkur_q2 = [kurtosis(x) for x in v2s]
dskew_diff = np.abs(np.array(dskew_q1) - np.array(dskew_q2)).reshape((-1,1))
dkur_diff = np.abs(np.array(dkur_q1) - np.array(dkur_q2)).reshape((-1,1))
return np.hstack((dcosine,dcityblock,dcanberra,deuclidean,dminkowski,dbraycurtis,dskew_diff,dkur_diff))
Example #5
| Source File: utils.py From teneto with GNU General Public License v3.0 | 5 votes |
|
def get_distance_function(requested_metric):
"""
This function returns a specified distance function.
Paramters
---------
requested_metric: str
Distance function. Can be any function in: https://docs.scipy.org/doc/scipy/reference/spatial.distance.html.
Returns
-------
requested_metric : distance function
"""
distance_options = {
'braycurtis': distance.braycurtis,
'canberra': distance.canberra,
'chebyshev': distance.chebyshev,
'cityblock': distance.cityblock,
'correlation': distance.correlation,
'cosine': distance.cosine,
'euclidean': distance.euclidean,
'sqeuclidean': distance.sqeuclidean,
'dice': distance.dice,
'hamming': distance.hamming,
'jaccard': distance.jaccard,
'kulsinski': distance.kulsinski,
'matching': distance.matching,
'rogerstanimoto': distance.rogerstanimoto,
'russellrao': distance.russellrao,
'sokalmichener': distance.sokalmichener,
'sokalsneath': distance.sokalsneath,
'yule': distance.yule,
}
if requested_metric in distance_options:
return distance_options[requested_metric]
else:
raise ValueError('Distance function cannot be found.')
Example #6
| Source File: taar_similarity.py From telemetry-airflow with Mozilla Public License 2.0 | 5 votes |
|
def similarity_function(x, y):
""" Similarity function for comparing user features.
This actually really should be implemented in taar.similarity_recommender
and then imported here for consistency.
"""
def safe_get(field, row, default_value):
# Safely get a value from the Row. If the value is None, get the
# default value.
return row[field] if row[field] is not None else default_value
# Extract the values for the categorical and continuous features for both
# the x and y samples. Use an empty string as the default value for missing
# categorical fields and 0 for the continuous ones.
x_categorical_features = [safe_get(k, x, "") for k in CATEGORICAL_FEATURES]
y_categorical_features = [safe_get(k, y, "") for k in CATEGORICAL_FEATURES]
x_continuous_features = [
float(safe_get(k, x, 0)) for k in CONTINUOUS_FEATURES
]
y_continuous_features = [
float(safe_get(k, y, 0)) for k in CONTINUOUS_FEATURES
]
# Here a larger distance indicates a poorer match between categorical variables.
j_d = distance.hamming(x_categorical_features, y_categorical_features)
j_c = distance.canberra(x_continuous_features, y_continuous_features)
# Take the product of similarities to attain a univariate similarity score.
# Add a minimal constant to prevent zero values from categorical features.
# Note: since both the distance function return a Numpy type, we need to
# call the |item| function to get the underlying Python type. If we don't
# do that this job will fail when performing KDE due to SPARK-20803 on
# Spark 2.2.0.
return abs((j_c + 0.001) * j_d).item()
Example #7
| Source File: taar_similarity.py From python_mozetl with MIT License | 5 votes |
|
def similarity_function(x, y):
""" Similarity function for comparing user features.
This actually really should be implemented in taar.similarity_recommender
and then imported here for consistency.
"""
def safe_get(field, row, default_value):
# Safely get a value from the Row. If the value is None, get the
# default value.
return row[field] if row[field] is not None else default_value
# Extract the values for the categorical and continuous features for both
# the x and y samples. Use an empty string as the default value for missing
# categorical fields and 0 for the continuous ones.
x_categorical_features = [safe_get(k, x, "") for k in CATEGORICAL_FEATURES]
x_continuous_features = [safe_get(k, x, 0) for k in CONTINUOUS_FEATURES]
y_categorical_features = [safe_get(k, y, "") for k in CATEGORICAL_FEATURES]
y_continuous_features = [safe_get(k, y, 0) for k in CONTINUOUS_FEATURES]
# Here a larger distance indicates a poorer match between categorical variables.
j_d = distance.hamming(x_categorical_features, y_categorical_features)
j_c = distance.canberra(x_continuous_features, y_continuous_features)
# Take the product of similarities to attain a univariate similarity score.
# Add a minimal constant to prevent zero values from categorical features.
# Note: since both the distance function return a Numpy type, we need to
# call the |item| function to get the underlying Python type. If we don't
# do that this job will fail when performing KDE due to SPARK-20803 on
# Spark 2.2.0.
return abs((j_c + 0.001) * j_d).item()
Example #8
| Source File: netsimile.py From netrd with MIT License | 4 votes |
|
def dist(self, G1, G2):
"""A scalable approach to network similarity.
A network similarity measure based on node signature distributions.
The results dictionary includes the underlying feature matrices in
`'feature_matrices'` and the underlying signature vectors in
`'signature_vectors'`.
Parameters
----------
G1, G2 (nx.Graph)
two undirected networkx graphs to be compared.
Returns
-------
dist (float)
the distance between `G1` and `G2`.
References
----------
.. [1] Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad,
Christos Faloutsos: NetSimile: A Scalable Approach to
Size-Independent Network Similarity. CoRR abs/1209.2684
(2012)
"""
# find the graph node feature matrices
G1_node_features = feature_extraction(G1)
G2_node_features = feature_extraction(G2)
# get the graph signature vectors
G1_signature = graph_signature(G1_node_features)
G2_signature = graph_signature(G2_node_features)
# the final distance is the absolute canberra distance
dist = abs(canberra(G1_signature, G2_signature))
self.results['feature_matrices'] = G1_node_features, G2_node_features
self.results['signature_vectors'] = G1_signature, G2_signature
self.results['dist'] = dist
return dist
