Ref : stackoverflow
The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).
For example for a directory structure like this:
new_project
├── antigravity.py
Ref : stackoverflow
The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).
For example for a directory structure like this:
new_project
├── antigravity.py
| for i in range(len(imfs1)): | |
| fig, ax = plt.subplots(figsize=(25,1)) | |
| fig = plt.plot(imfs1[i], label = "IMF{}".format(i)) | |
| plt.legend() | |
| plt.show() |
| App Insights: 8de2bc6e-8885-43c4-892d-ba8440bb6026 |
| ''' | |
| Author : Mohcine Madkour | |
| Email : mohcine.madkour@gmail.com | |
| Date : 10-Sep-2014 | |
| Description: Simulations of Schelling's seggregation model | |
| http://www.binpress.com/tutorial/introduction-to-agentbased-models-an-implementation-of-schelling-model-in-python/144 | |
| ''' | |
| import matplotlib.pyplot as plt | |
| import itertools |
| ''' | |
| Author : Mohcine Madkour | |
| Email : mohcine.madkour@gmail.com | |
| Date : 10-Sep-2014 | |
| Description: Simulations of Schelling's seggregation model | |
| http://www.binpress.com/tutorial/introduction-to-agentbased-models-an-implementation-of-schelling-model-in-python/144 | |
| ''' | |
| import matplotlib.pyplot as plt | |
| import itertools |
| # prepare for modeling | |
| X_train = train.drop(['id', 'target'], axis=1) | |
| y_train = train['target'] | |
| X_test = test.drop(['id'], axis=1) | |
| # scaling data | |
| scaler = StandardScaler() | |
| X_train = scaler.fit_transform(X_train) | |
| X_test = scaler.transform(X_test) |
| penalty = ['l1', 'l2'] | |
| C = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000] | |
| class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}] | |
| solver = ['liblinear', 'saga'] | |
| param_grid = dict(penalty=penalty, | |
| C=C, | |
| class_weight=class_weight, | |
| solver=solver) |
| loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'] | |
| penalty = ['l1', 'l2', 'elasticnet'] | |
| alpha = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000] | |
| learning_rate = ['constant', 'optimal', 'invscaling', 'adaptive'] | |
| class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}] | |
| eta0 = [1, 10, 100] | |
| param_distributions = dict(loss=loss, | |
| penalty=penalty, | |
| alpha=alpha, |