black

examples/daal4py/adaboost.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Adaboost example for shared memory systems
 
@@ -25,14 +25,15 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/adaboost_train.csv"
     testfile = "./data/batch/adaboost_test.csv"
     nClasses = 2
@@ -56,7 +57,7 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     # The prediction result provides prediction
     assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
-    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=',', ndmin=2)
+    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=",", ndmin=2)
     assert np.allclose(predict_result.prediction, ptdata)
 
     return (train_result, predict_result, ptdata)
@@ -67,6 +68,6 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("\nGround truth (first 20 observations):\n", ptdata[:20])
     print(
         "Adaboost classification results: (first 20 observations):\n",
-        predict_result.prediction[:20]
+        predict_result.prediction[:20],
     )
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/adagrad_mse.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py AdaGrad (Adaptive Subgradient Method) example for shared memory systems
 # using Mean Squared Error objective function
@@ -26,14 +26,15 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/mse.csv"
     # Read the data, let's have 3 independent variables
     data = readcsv(infile, range(3))
@@ -47,11 +48,13 @@ def main(readcsv=read_csv, method='defaultDense'):
     # configure an AdaGrad object
     lr = np.array([[1.0]], dtype=np.double)
     niters = 1000
-    sgd_algo = d4p.optimization_solver_adagrad(mse_algo,
-                                               learningRate=lr,
-                                               accuracyThreshold=0.0000001,
-                                               nIterations=niters,
-                                               batchSize=1)
+    sgd_algo = d4p.optimization_solver_adagrad(
+        mse_algo,
+        learningRate=lr,
+        accuracyThreshold=0.0000001,
+        nIterations=niters,
+        batchSize=1,
+    )
 
     # finally do the computation
     inp = np.array([[8], [2], [1], [4]], dtype=np.double)
@@ -67,4 +70,4 @@ def main(readcsv=read_csv, method='defaultDense'):
     res = main()
     print("\nMinimum:\n", res.minimum)
     print("\nNumber of iterations performed:\n", res.nIterations[0][0])
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/association_rules.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py assiciation rules example for shared memory systems
 
@@ -25,14 +25,15 @@
     import pandas
 
     def read_csv(f, c=None, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c=None, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/apriori.csv"
 
     # configure a association_rules object
@@ -58,6 +59,6 @@ def main(readcsv=read_csv, method='defaultDense'):
 
 if __name__ == "__main__":
     result1 = main()
-    print('Confidence: (20 first)')
+    print("Confidence: (20 first)")
     print(result1.confidence[0:20])
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/bacon_outlier.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py outlier detection bacon example for shared memory systems
 
@@ -25,14 +25,15 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     # Input file
     infile = "./data/batch/outlierdetection.csv"
 
@@ -56,4 +57,4 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     print("\nInput data\n", data)
     print("\nOutlier detection result (Bacon method) weights:\n", res.weights)
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/bf_knn_classification.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2020 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Brute Force KNN example for shared memory systems
 
@@ -27,17 +27,18 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     # Input data set parameters
-    train_file = os.path.join('data', 'batch', 'k_nearest_neighbors_train.csv')
-    predict_file = os.path.join('data', 'batch', 'k_nearest_neighbors_test.csv')
+    train_file = os.path.join("data", "batch", "k_nearest_neighbors_train.csv")
+    predict_file = os.path.join("data", "batch", "k_nearest_neighbors_test.csv")
 
     # Read data. Let's use 5 features per observation
     nFeatures = 5
@@ -71,6 +72,5 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("Brute Force kNN classification results:")
     print("Ground truth(observations #30-34):\n", predict_labels[30:35])
     print(
-        "Classification results(observations #30-34):\n",
-        predict_result.prediction[30:35]
+        "Classification results(observations #30-34):\n", predict_result.prediction[30:35]
     )

examples/daal4py/brownboost.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Brownboost example for shared memory systems
 
@@ -25,14 +25,15 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/brownboost_train.csv"
     testfile = "./data/batch/brownboost_test.csv"
 
@@ -55,7 +56,7 @@ def main(readcsv=read_csv, method='defaultDense'):
 
     # The prediction result provides prediction
     assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
-    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=',', ndmin=2)
+    ptdata = np.loadtxt(testfile, usecols=range(20, 21), delimiter=",", ndmin=2)
     assert np.allclose(predict_result.prediction, ptdata)
 
     return (train_result, predict_result, ptdata)
@@ -66,6 +67,6 @@ def main(readcsv=read_csv, method='defaultDense'):
     print("\nGround truth (first 20 observations):\n", ptdata[:20])
     print(
         "Brownboost classification results: (first 20 observations):\n",
-        predict_result.prediction[:20]
+        predict_result.prediction[:20],
     )
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/cholesky.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py cholesky example for shared memory systems
 
@@ -25,14 +25,15 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
+def main(readcsv=read_csv, method="defaultDense"):
     infile = "./data/batch/cholesky.csv"
 
     # configure a cholesky object
@@ -46,4 +47,4 @@ def main(readcsv=read_csv, method='defaultDense'):
 if __name__ == "__main__":
     result = main()
     print("\nFactor:\n", result.choleskyFactor)
-    print('All looks good!')
+    print("All looks good!")

examples/daal4py/correlation_distance.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 # daal4py correlation distance example for shared memory systems
 
@@ -27,21 +27,22 @@
     import pandas
 
     def read_csv(f, c, t=np.float64):
-        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
+        return pandas.read_csv(f, usecols=c, delimiter=",", header=None, dtype=t)
+
 except ImportError:
     # fall back to numpy loadtxt
     def read_csv(f, c, t=np.float64):
-        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)
+        return np.loadtxt(f, usecols=c, delimiter=",", ndmin=2)
 
 
-def main(readcsv=read_csv, method='defaultDense'):
-    data = readcsv(os.path.join('data', 'batch', 'distance.csv'), range(10))
+def main(readcsv=read_csv, method="defaultDense"):
+    data = readcsv(os.path.join("data", "batch", "distance.csv"), range(10))
 
     # Create algorithm to compute correlation distance (no parameters)
     algorithm = d4p.correlation_distance()
 
     # Computed correlation distance with file or numpy array
-    res1 = algorithm.compute(os.path.join('data', 'batch', 'distance.csv'))
+    res1 = algorithm.compute(os.path.join("data", "batch", "distance.csv"))
     res2 = algorithm.compute(data)
 
     assert np.allclose(res1.correlationDistance, res2.correlationDistance)
@@ -53,6 +54,6 @@ def main(readcsv=read_csv, method='defaultDense'):
     res = main()
     print(
         "\nCorrelation distance (first 15 rows/columns):\n",
-        res.correlationDistance[0:15, 0:15]
+        res.correlationDistance[0:15, 0:15],
     )
     print("All looks good!")