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4.2. Selected Population
4.2.1. Remove Excluded Population, Remove Unused Features
<i>Nothing to do!<i/>
<br/>
<font style="font-weight:bold;color:red">Notes: </font>
- Ideally the features must be configured before generating the CSV feature file, as it is very inefficient to derive new features at this stage
- This... | # Exclusion of unused features
# excluded = [name for name in features_input.columns if name not in features_names_group.keys()]
# features_input = features_input.drop(excluded, axis=1)
# print("Number of columns: ", len(features_input.columns), "; Total records: ", len(features_input.index)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
5. Set Samples & Target Features
5.1. Set Features
5.1.1. Train & Test Samples
Set the samples | frac_train = 0.50
replace = False
random_state = 100
nrows = len(features_input.index)
features = {"train": dict(), "test": dict()}
features["train"] = features_input.sample(frac=frac_train, replace=False, random_state=100)
features["test"] = features_input.drop(features["train"].index)
features["train"] = features["... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:red">Clean-Up</font> | features_input = None
gc.collect() | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
5.1.2. Independent & Target variable¶
Set independent, target & ID features | target_labels = list(features_types_group["TARGET"])
target_id = ["patientID"]
features["train_indep"] = dict()
features["train_target"] = dict()
features["train_id"] = dict()
features["test_indep"] = dict()
features["test_target"] = dict()
features["test_id"] = dict()
# Independent and target features
def set_featur... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Verify features visually | display(pd.concat([features["train_id"].head(), features["train_target"].head(), features["train_indep"].head()], axis=1))
display(pd.concat([features["test_id"].head(), features["test_target"].head(), features["test_indep"].head()], axis=1)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:red">Clean-Up</font> | del features["train"]
del features["test"]
gc.collect() | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
5.5. Save Samples
Serialise & save the samples before any feature transformation.
<br/>This snapshot of the samples may be used for the population profiling | file_name = "Step_05_Features"
readers_writers.save_serialised_compressed(path=CONSTANTS.io_path, title=file_name, objects=features)
# print
print("Number of columns: ", len(features["train_indep"].columns),
"features: {train: ", len(features["train_indep"]), ", test: ", len(features["test_indep"]), "}") | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
5.2. Remove - Near Zero Variance
In order to reduce sparseness and invalid features, highly stationary ones were withdrawn. The features that had constant counts less than or equal a threshold were ltered out, to exclude highly constants and near-zero variances.
The near zero variance rules are presented in below:
- F... | thresh_unique_cut = 100
thresh_freq_cut = 1000
excludes = []
file_name = "Step_05_Preprocess_NZV_config"
features["train_indep"], o_summaries = preprocess.near_zero_var_df(df=features["train_indep"],
excludes=excludes,
... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
5.3. Remove Highly Linearly Correlated
In this step, features that were highly linearly correlated were excluded.
<font style="font-weight:bold;color:red">Configure:</font> the function
- A numeric value for the pair-wise absolute correlation cutoff. e.g. 0.95
<br/>	 → thresh_corr_cut | thresh_corr_cut = 0.95
excludes = list(features_types_group["CATEGORICAL"])
file_name = "Step_05_Preprocess_Corr_config"
features["train_indep"], o_summaries = preprocess.high_linear_correlation_df(df=features["train_indep"],
excludes=excludes,
... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
5.4. Descriptive Statistics
Produce a descriptive stat report of 'Categorical', 'Continuous', & 'TARGET' features | # columns
file_name = "Step_05_Data_ColumnNames_Train"
readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name,
data=list(features["train_indep"].columns.values), append=False)
# Sample - Train
file_name = "Step_05_Stats_Categorical_Train"
o_stats = preprocess.stats_discrete_df(df=fe... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
6. Recategorise & Transform
Verify features visually | display(pd.concat([features["train_id"].head(), features["train_target"].head(), features["train_indep"].head()], axis=1))
display(pd.concat([features["test_id"].head(), features["test_target"].head(), features["test_indep"].head()], axis=1)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.1. Recategorise
Define the factorisation function to generate dummy features for the categorical features. | def factorise_settings(max_categories_frac, min_categories_num, exclude_zero):
categories_dic = dict()
labels_dic = dict()
dtypes_dic = dict()
dummies = []
for f_name in features_types_group["CATEGORICAL"]:
if f_name in features["train_indep"]:
# find top & valid states
... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Select categories: by order of freq., max_categories_frac, & max_categories_num
<br/><font style="font-weight:bold;color:red">Configure:</font> The input arguments are:
- Specify the maximum number of categories a feature can have
<br/>	 → max_categories_frac
- Specify the minimum number of categories a featur... | max_categories_frac = 0.90
min_categories_num = 1
exclude_zero = False # if possible remove state zero
categories_dic, labels_dic, dtypes_dic, features_types_group["DUMMIES"] = \
factorise_settings(max_categories_frac, min_categories_num, exclude_zero) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Manually add dummy variables to the dataframe & remove the original Categorical variables | features["train_indep_temp"] = preprocess.factoring_feature_wise(features["train_indep"], categories_dic, labels_dic, dtypes_dic, threaded=False)
features["test_indep_temp"] = preprocess.factoring_feature_wise(features["test_indep"], categories_dic, labels_dic, dtypes_dic, threaded=False)
# print
print("Number of colu... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Verify features visually | display(pd.concat([features["train_id"].head(), features["train_target"].head(), features["train_indep_temp"].head()], axis=1))
display(pd.concat([features["test_id"].head(), features["test_target"].head(), features["test_indep_temp"].head()], axis=1)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Set | features["train_indep"] = features["train_indep_temp"].copy(True)
features["test_indep"] = features["test_indep_temp"].copy(True) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:red">Clean-Up</font> | del features["train_indep_temp"]
del features["test_indep_temp"]
gc.collect() | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.2. Remove - Near Zero Variance
Optional: Remove more features with near zero variance, after the factorisation step.
<font style="font-weight:bold;color:red">Configure:</font> the function | # the cutoff for the percentage of distinct values out of the number of total samples (upper limit). e.g. 10 * 100 / 100
thresh_unique_cut = 100
# the cutoff for the ratio of the most common value to the second most common value (lower limit). eg. 95/5
thresh_freq_cut = 1000
excludes = []
file_name = "Step_06_Preproce... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.3. Remove Highly Linearly Correlated
Optional: Remove more features with highly linearly correlated, after the factorisation step.
<font style="font-weight:bold;color:red">Configure:</font> the function | # A numeric value for the pair-wise absolute correlation cutoff. e.g. 0.95
thresh_corr_cut = 0.95
excludes = []
file_name = "Step_06_Preprocess_Corr_config"
features["train_indep"], o_summaries = preprocess.high_linear_correlation_df(df=features["train_indep"],
... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.4. Descriptive Statsistics
Produce a descriptive stat report of 'Categorical', 'Continuous', & 'TARGET' features | # columns
file_name = "Step_06_4_Data_ColumnNames_Train"
readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name,
data=list(features["train_indep"].columns.values), append=False)
# Sample - Train
file_name = "Step_06_4_Stats_Categorical_Train"
o_stats = preprocess.stats_discrete_df(d... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.5. Transformations
Verify features visually | display(pd.concat([features["train_id"].head(), features["train_target"].head(), features["train_indep"].head()], axis=1))
display(pd.concat([features["test_id"].head(), features["test_target"].head(), features["test_indep"].head()], axis=1)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Tranformation:</font> scale
<font style="font-weight:bold;color:brown">Note:</font>: It is highly resource intensive | transform_type = "scale"
kwargs = {"with_mean": True}
method_args = dict()
excludes = list(features_types_group["CATEGORICAL"]) + list(features_types_group["DUMMIES"])
features["train_indep"], method_args = preprocess.transform_df(df=features["train_indep"], excludes=excludes,
... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Tranformation:</font> Yeo-Johnson
<font style="font-weight:bold;color:brown">Note:</font>: It is highly resource intensive | transform_type = "yeo_johnson"
kwargs = {"lmbda": -0.5, "derivative": 0, "epsilon": np.finfo(np.float).eps, "inverse": False}
method_args = dict()
excludes = list(features_types_group["CATEGORICAL"]) + list(features_types_group["DUMMIES"])
features["train_indep"], method_args = preprocess.transform_df(df=features["tra... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
6.6. Summary Statistics
Produce a descriptive stat report of 'Categorical', 'Continuous', & 'TARGET' features | # Statsistics report for 'Categorical', 'Continuous', & 'TARGET' variables
# columns
file_name = "Step_06_6_Data_ColumnNames_Train"
readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name,
data=list(features["train_indep"].columns.values), append=False)
# Sample - Train
file_name = "... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
7. Rank & Select Features
<font style="font-weight:bold;color:red">Configure:</font> the general settings | # select the target variable
target_feature = "label365" # "label30", "label365"
# number of trials
num_trials = 1
model_rank = dict()
o_summaries_df = dict() | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.1. Define
<font style="font-weight:bold;color:blue">Ranking Method:</font> Random forest classifier (Brieman)
<br/>Define a set of classifiers with different settings, to be used in feature ranking trials. | def rank_random_forest_brieman(features_indep_arg, features_target_arg, num_trials):
num_settings = 3
o_summaries_df = [pd.DataFrame({'Name': list(features_indep_arg.columns.values)}) for _ in range(num_trials * num_settings)]
model_rank = [None] * (num_trials * num_settings)
# trials
for i in ran... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Ranking Method:</font> Gradient Boosted Regression Trees (GBRT)
<br/>Define a set of classifiers with different settings, to be used in feature ranking trials. | def rank_gbrt(features_indep_arg, features_target_arg, num_trials):
num_settings = 3
o_summaries_df = [pd.DataFrame({'Name': list(features_indep_arg.columns.values)}) for _ in range(num_trials * num_settings)]
model_rank = [None] * (num_trials * num_settings)
# trials
for i in range(num_trials): ... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Ranking Method:</font> Randomized Logistic Regression
<br/>Define a set of classifiers with different settings, to be used in feature ranking trials. | def rank_randLogit(features_indep_arg, features_target_arg, num_trials):
num_settings = 3
o_summaries_df = [pd.DataFrame({'Name': list(features_indep_arg.columns.values)}) for _ in range(num_trials * num_settings)]
model_rank = [None] * (num_trials * num_settings)
# trials
for i in range(num_trial... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.2. Run
Run one or more feature ranking methods and trials
<font style="font-weight:bold;color:blue">Ranking Method:</font> Random forest classifier (Brieman)
<font style="font-weight:bold;color:brown">Note:</font>: It is moderately resource intensive | rank_model = "rfc"
model_rank[rank_model] = dict()
o_summaries_df[rank_model] = dict()
model_rank[rank_model], o_summaries_df[rank_model] = rank_random_forest_brieman(
features["train_indep"], features["train_target"][target_feature], num_trials) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Ranking Method:</font> Gradient Boosted Regression Trees (GBRT)
<font style="font-weight:bold;color:brown">Note:</font>: It is moderately resource intensive | rank_model = "gbrt"
model_rank[rank_model] = dict()
o_summaries_df[rank_model] = dict()
model_rank[rank_model], o_summaries_df[rank_model] = rank_gbrt(
features["train_indep"], features["train_target"][target_feature], num_trials) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:blue">Ranking Method</font>: Randomized Logistic Regression
<font style="font-weight:bold;color:brown">Note:</font>: It is moderately resource intensive | rank_model = "randLogit"
model_rank[rank_model] = dict()
o_summaries_df[rank_model] = dict()
model_rank[rank_model], o_summaries_df[rank_model] = rank_randLogit(
features["train_indep"], features["train_target"][target_feature], num_trials) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.3. Summaries | # combine scores
def rank_summarise (features_arg, o_summaries_df_arg):
summaries_temp = {'Order_avg': [], 'Order_max': [], 'Order_min': [], 'Importance_avg': []}
summary_order = []
summary_importance = []
for f_name in list(features_arg.columns.values):
for i in range(len(o_summaries_df_a... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Save | for rank_model in model_rank.keys():
file_name = "Step_07_Model_Train_model_rank_" + rank_model
readers_writers.save_serialised_compressed(path=CONSTANTS.io_path, title=file_name, objects=model_rank[rank_model])
file_name = "Step_07_Model_Train_model_rank_summaries_" + rank_model
readers_writers.sa... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.4. Select Top Features
<font style="font-weight:bold;color:red">Configure:</font> the selection method | rank_model = "rfc"
file_name = "Step_07_Top_Features_" + rank_model
rank_top_features_max = 400
rank_top_features_score_min = 0.1 * (10 ^ -20)
# sort features
features_names_selected = summaries_df[rank_model]['Name'][summaries_df[rank_model]['Order_avg'] >= rank_top_features_score_min]
features_names_selected = (feat... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Save | # save to CSV
readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name, data=features_names_selected, append=False, header=False)
# print
print("Number of columns: ", len(features["train_indep"].columns))
print("features: {train: ", len(features["train_indep"]), ", test: ", len(features["test_indep"]), "... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:red">Configure</font>: the selected feature manually if it isnecessary! | file_name = "Step_07_Top_Features_rfc_adhoc"
features_names_selected = readers_writers.load_csv(path=CONSTANTS.io_path, title=file_name, dataframing=False)[0]
features_names_selected = [f.replace("\n", "") for f in features_names_selected]
display(pd.DataFrame(features_names_selected)) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Verify the top features visually | # print
print("Number of columns: ", len(features["train_indep"].columns),
";\nNumber of top columns: ", len(features["train_indep"][features_names_selected].columns))
print("features: {train: ", len(features["train_indep"][features_names_selected]), ", test: ", len(features["test_indep"][features_names_sele... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.5. Summary Statistics
Produce a descriptive stat report of 'Categorical', 'Continuous', & 'TARGET' features | # columns
file_name = "Step_07_Data_ColumnNames_Train"
readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name,
data=list(features["train_indep"][features_names_selected].columns.values), append=False)
# Sample - Train
file_name = "Step_07_Stats_Categorical_Train"
o_stats = preproces... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
7.6. Save Features | file_name = "Step_07_Features"
readers_writers.save_serialised_compressed(path=CONSTANTS.io_path, title=file_name, objects=features)
# print
print("File size: ", os.stat(os.path.join(CONSTANTS.io_path, file_name + ".bz2")).st_size)
print("Number of columns: ", len(features["train_indep"].columns))
print("feature... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
<br/><br/>
8. Model
<font style="font-weight:bold;color:orange">Load a Saved Samples and Features Ranking:</font>
<br/> It is an optional step. The step loads the serialised & compressed outputs of Step-7. | # open fetures
file_name = "Step_07_Features"
features = readers_writers.load_serialised_compressed(path=CONSTANTS.io_path, title=file_name)
# print
print("File size: ", os.stat(os.path.join(CONSTANTS.io_path, file_name + ".bz2")).st_size)
print("Number of columns: ", len(features["train_indep"].columns))
print(... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
8.1. Initialise
8.1.1. Algorithms
<font style="font-weight:bold;color:red">Configure:</font> the trianing algorithm
<font style="font-weight:bold;color:brown">Algorithm 1</font>: Random Forest | method_name = "rfc"
kwargs = {"n_estimators": 20, "criterion": 'gini', "max_depth": None, "min_samples_split": 100,
"min_samples_leaf": 50, "min_weight_fraction_leaf": 0.0, "max_features": 'auto',
"max_leaf_nodes": None, "bootstrap": True, "oob_score": False, "n_jobs": -1, "random_state": None,
"verbose": 0... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 2</font>: Logistic Regression | method_name = "lr"
kwargs = {"penalty": 'l1', "dual": False, "tol": 0.0001, "C": 1, "fit_intercept": True, "intercept_scaling": 1,
"class_weight": None, "random_state": None, "solver": 'liblinear', "max_iter": 100, "multi_class": 'ovr',
"verbose": 0, "warm_start": False, "n_jobs": -1} | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 3</font>: Logistic Cross-Validation | method_name = "lr_cv"
kwargs = {"Cs": 10, "fit_intercept": True, "cv": None, "dual": False, "penalty": 'l2', "scoring": None,
"solver": 'lbfgs', "tol": 0.0001, "max_iter": 10, "class_weight": None, "n_jobs": -1, "verbose": 0,
"refit": True, "intercept_scaling": 1.0, "multi_class": "ovr", "random_s... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 4</font>: Neural Network | method_name = "nn"
kwargs = {"solver": 'lbfgs', "alpha": 1e-5, "hidden_layer_sizes": (5, 2), "random_state": 1} | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 5</font>: k-Nearest Neighbourhood | method_name = "knc"
kwargs = {"n_neighbors": 5, "weights": 'distance', "algorithm": 'auto', "leaf_size": 30,
"p": 2, "metric": 'minkowski', "metric_params": None, "n_jobs": -1} | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 6</font>: Decision Tree | method_name = "dtc"
kwargs = {"criterion": 'gini', "splitter": 'best', "max_depth": None, "min_samples_split": 30,
"min_samples_leaf": 30, "min_weight_fraction_leaf": 0.0, "max_features": None,
"random_state": None, "max_leaf_nodes": None, "class_weight": None, "presort": False} | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 7</font>: Gradient Boosting Classifier | method_name = "gbc"
kwargs = {"loss": 'deviance', "learning_rate": 0.1, "n_estimators": 100, "subsample": 1.0, "min_samples_split": 30,
"min_samples_leaf": 30, "min_weight_fraction_leaf": 0.0, "max_depth": 3, "init": None, "random_state": None,
"max_features": None, "verbose": 0, "max_leaf_nodes": None,... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<font style="font-weight:bold;color:brown">Algorithm 8</font>: Naive Bayes<br/>
Note: features must be positive | method_name = "nb"
training_method = TrainingMethod(method_name)
kwargs = {"alpha": 1.0, "fit_prior": True, "class_prior": None} | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
<br/><br/>
8.1.2. Other Settings
<font style="font-weight:bold;color:red">Configure:</font> other modelling settings | # select the target variable
target_feature = "label365" # "label30" , "label365"
# file name
file_name = "Step_09_Model_" + method_name + "_" + target_feature
# initialise
training_method = TrainingMethod(method_name) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
8.1.3. Features | sample_train = features["train_indep"][features_names_selected] # features["train_indep"][features_names_selected], features["train_indep"]
sample_train_target = features["train_target"][target_feature] # features["train_target"][target_feature]
sample_test = features["test_indep"][features_names_selected] # features["... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
8.3. Fit
Fit the model, using the train sample | o_summaries = dict()
# Fit
model = training_method.train(sample_train, sample_train_target, **kwargs)
training_method.save_model(path=CONSTANTS.io_path, title=file_name)
# load model
# training_method.load(path=CONSTANTS.io_path, title=file_name)
# short summary
o_summaries = training_method.train_summaries() | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Predict & report performance, using the train sample | o_summaries = dict()
# predict
model = training_method.predict(sample_train, "train")
# short summary
o_summaries = training_method.predict_summaries(pd.Series(sample_train_target), "train")
# Print the main performance statistics
for k in o_summaries.keys():
print(k, o_summaries[k])
# Print the by risk-bands o... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
8.4. Predict
Predict & report performance, using the test sample | o_summaries = dict()
# predict
model = training_method.predict(sample_test, "test")
# short summary
o_summaries = training_method.predict_summaries(pd.Series(sample_test_target), "test")
# Print the main performance statistics
for k in o_summaries.keys():
print(k, o_summaries[k])
# Print the by risk-bands of a ... | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
8.5. Cross-Validation
Perform k-fold cross-validation | o_summaries = dict()
score = training_method.cross_validate(sample_test, sample_test_target, scoring="neg_mean_squared_error", cv=10)
# short summary
o_summaries = training_method.cross_validate_summaries()
print("Scores: ", o_summaries) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
8.6. Save
Save the training model. | training_method.save_model(path=CONSTANTS.io_path, title=file_name) | TCARER_Basic.ipynb | mesgarpour/T-CARER | apache-2.0 |
Calcolo matrice adiacenza
Calcolo il raggio medio che definisce Roma entro il raccordo anulare
NB: da verificare che distanza euclidea non crei troppi problemi | colosseo = (41.890173, 12.492331)
raccordo = [(41.914456, 12.615807),(41.990672, 12.502714),(41.793883, 12.511297),(41.812566, 12.396628),(41.956277, 12.384611)]
raggi = []
def geodesicDistance(A, B=colosseo):
return geopy.distance.vincenty(A, B).meters
raggioTerra = 6372795
def euclidDistance(A, B=colosseo):
... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Popolo il dataframe e faccio una prima grossa scrematura | dataframe = pandas.read_csv("/home/protoss/Documenti/Siscomp_datas/data/cell_towers.csv")
#dataframe = pandas.read_csv("/home/protoss/Documenti/SistemiComplessi/data/cell_towers_diff-2016012100.csv")
#dataframe
criterioMCC = dataframe.mcc == 222
criterioMinsamples = dataframe.samples > 1
italydoitcleaner = dataframe[cr... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Seleziono le antenne in Roma e faccio dei .csv appositi | #istruzione che fa selezione alcune righe con criteri su alcune colonne,
#ne seleziona alcune e restituisce un array nompy di valori desiderati
coordinate = dataframe[criterioMCC & criterioMinsamples][['lat', 'lon']].values
%time distanza = numpy.array(map(geodesicDistance, coordinate), dtype = int)
raggiomedioGeo =... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Prendo le antenne di Roma e faccio matrice adiacenza | #definisco la funzione che mi calcola la matrice di adiacenza
def matriceSupEuclid(datiCoordinate, datiRaggi):
a = numpy.zeros((numdati,numdati), dtype=int)
for i in xrange(numdati):
for j in xrange(numdati-i-1):
sommaraggi = datiRaggi[i] + datiRaggi[j+i+1]
#è equivalente a un if... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Faccio grafo e calcolo distr grado | #for azienda in gestore:
#italydoitcleaner['distanze'] = distanza
#romaCell.to_csv("../data/Roma_towers.csv")
adiacenzaRoma = numpy.genfromtxt("/home/protoss/Documenti/Siscomp_datas/data/AdiacenzaEuclidea_Roma.csv",delimiter=',',dtype='int')
adiacenzaTim = numpy.genfromtxt("/home/protoss/Documenti/Siscomp_datas/data/A... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Topologia iniziale con networkx (molto lento) | gestore = ["Tim", "Vodafone", "Wind", "Tre"]
def topologyNetx(gestore):
adiacenza = numpy.genfromtxt("/home/protoss/Documenti/Siscomp_datas/data/AdiacenzaEuclidea_{0}.csv".format(gestore),delimiter=',',dtype='int')
grafo = networkx.Graph(adiacenza)
c = networkx.average_clustering(grafo)
d = networkx.d... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
NB. num antenne
* TIM - 1756
* Vodafone - 1771
* Wind - 2365
* 3 - 1395
Tot antenne: 6571
TODO:
Prendere array coordinate ✔
fare array distanze ✔
mettere colonna distanze in dataframe ✔
selezionare righe con variabile compresa entro raggiomedio ✔
fare un nuovo dataframe ✔
escludere tu... | colori = ['#4d4d4d', '#004184','#ff3300','#ff8000','#018ECC']
paletta = seaborn.color_palette(palette = colori)
seaborn.palplot(paletta)
paletta = seaborn.color_palette(palette = 'muted')
seaborn.palplot(paletta)
paletta = seaborn.color_palette(palette = 'bright')
seaborn.palplot(paletta)
paletta = seaborn.color_palett... | src/Adiacenza, grafo e grado.ipynb | FedericoMuciaccia/SistemiComplessi | mit |
Kruk-Jaroniec-Sayari
$\displaystyle{t_\mathrm{i} = \left(\frac{C_\mathrm{1}}{C_\mathrm{2}-\log{(P_\mathrm{rel,\ i})}}\right)^{C_\mathrm{3}}}$
where typically $C_\mathrm{1} = 60.6500$, $C_\mathrm{2} = 0.03071$, and $C_\mathrm{3} = 0.3968$
M. Kruk, M. Jaroniec, and A. Sayari. "Application of Large Pore MCM-41 Molecular S... | plots.plotThickness( Prel, t.KrukJaroniecSayari()(Prel), 'Kruk-Jaroniec-Sayari' ) | documentation/thickness.ipynb | lowks/micromeritics | gpl-3.0 |
Halsey
The Halsey equation assumes the adsorbed liquid monolayer has the same density and packing as the normal liquid. The values 3.54
and 5.00 are empirical and are user adjustable.
$\displaystyle{t_\mathrm{i} = C_\mathrm{1}\left(\frac{C_\mathrm{2}}{\ln{(P_\mathrm{rel,\ i})}}\right)^{C_\mathrm{3}}}$
where typically $... | plots.plotThickness( Prel, t.Halsey()(Prel), 'Halsey') | documentation/thickness.ipynb | lowks/micromeritics | gpl-3.0 |
Harkins and Jura
The work of Harkins and Jura has shown that a plot of $\log{(P_\mathrm{rel})} = B - A/V_\mathrm{a}^\mathrm{2}$ returns a linear region where the film is condensed (where B is the intercept and A is the slope of the linear region). The relative pressure can be related to the statistical thickness $(t_\... | plots.plotThickness( Prel, t.HarkinsJura()(Prel), 'Harkins and Jura') | documentation/thickness.ipynb | lowks/micromeritics | gpl-3.0 |
Broekhoff-de Boer
$\displaystyle{\log{(P_\mathrm{rel,\ i})} = \frac{C_\mathrm{1}}{t_\mathrm{i}^{2}}+C_\mathrm{2}e^{C_\mathrm{3}t_\mathrm{i}}}$
where typically $C_\mathrm{1} = -16.1100$, $C_\mathrm{2} = 0.1682$, and $C_\mathrm{3} = -0.1137$
Broekhoff, J.C.P. and de Boer, J.H., "The Surface Area in Intermediate Pores," P... | plots.plotThickness( Prel, t.BroekhoffDeBoer()(Prel), 'Broekhoff-de Boer') | documentation/thickness.ipynb | lowks/micromeritics | gpl-3.0 |
Carbon Black STSA
$\displaystyle{t_\mathrm{i} = C_\mathrm{1}(P_\mathrm{rel,\ i})^2+C_\mathrm{2}(P_\mathrm{rel,\ i})+C_\mathrm{3}}$
where typically $C_\mathrm{1} = 2.9800$, $C_\mathrm{2} = 6.4500$, and $C_\mathrm{3} = 0.8800$ | plots.plotThickness( Prel, t.CarbonBlackSTSA()(Prel), 'Carbon Black STSA') | documentation/thickness.ipynb | lowks/micromeritics | gpl-3.0 |
We'll use some of the data that comes pre-packaged with statsmodels to demonstrate the library functionality. The data set below comprises incomplete, daily measurements of CO2 levels in Hawaii.
Note: at the time of this writing, the current release of statsmodels includes a utility method for loading these datasets as... | def get_statsmodels_df():
"""Return packaged data in a pandas.DataFrame"""
# some hijinks to get around outdated statsmodels code
dataset = sm.datasets.co2.load()
start = dataset.data['date'][0].decode('utf-8')
index = pd.date_range(start=start, periods=len(dataset.data), freq='W-SAT')
obs = pd.... | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
Because it's based on some existing statsmodels functionality, STLDecompose requires two things of the input dataframe:
1. continuous observations (no missing data points)
2. a pandas DateTimeIndex
Since these are both very situation-dependent, we leave it to the user to define how they want to acheive these goals - pa... | obs = (obs
.resample('D')
.mean()
.interpolate('linear'))
obs.head(10)
obs.index
obs.head(1000).plot() | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
Decompose
One of the primary pieces of functionality is the STL decomposition. The associated method requires the observation frame, and the primary (largest) period of seasonality. This period is specified in terms of index positions, and so care is needed for the user to correctly specify the periodicity in terms of ... | decomp = decompose(obs, period=365)
decomp | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
The resulting object is an extended version of the statsmodels.tsa.seasonal.DecomposeResult. Like the statsmodels object, the arrays of values are available on the object (the observations; and the trend, seasonal, and residual components). An extra attribute (the average seasonal cycle) has been added for the purpose ... | decomp.plot(); | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
Forecast
While the STL decomposition is interesting on its own, STLDecompose also provides some relatively naive capabilities for using the decomposition to forecast based on our observations.
We'll use the same data set, but pretend that we only had the first two third of observations. Then we can compare our forecas... | len(obs)
short_obs = obs.head(10000)
# apply the decomp to the truncated observation
short_decomp = decompose(short_obs, period=365)
short_decomp | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
The forecast() method requires the following arguments:
- the previously fit DecomposeResult
- the number of steps forward for which we'd like the forecast
- the specific forecasting function to be applied to the decomposition
There are a handful of predefined functions that can be imported from the stldecompose.foreca... | fcast = forecast(short_decomp, steps=8000, fc_func=drift)
fcast.head() | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
If desired, we can then plot the corresponding components of the observation and forecast to check and verify the results. | plt.plot(obs, '--', label='truth')
plt.plot(short_obs, '--', label='obs')
plt.plot(short_decomp.trend, ':', label='decomp.trend')
plt.plot(fcast, '-', label=fcast.columns[0])
plt.xlim('1970','2004'); plt.ylim(330,380);
plt.legend(); | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
To include the estimated seasonal component in the forecast, use the boolean seasonal keyword. | fcast = forecast(short_decomp, steps=8000, fc_func=drift, seasonal=True)
plt.plot(obs, '--', label='truth')
plt.plot(short_obs, '--', label='obs')
plt.plot(short_decomp.trend, ':', label='decomp.trend')
plt.plot(fcast, '-', label=fcast.columns[0])
plt.xlim('1970','2004'); plt.ylim(330,380);
plt.legend();
fcast.head(... | STL-usage-example.ipynb | jrmontag/STLDecompose | mit |
RiiDataFrame has an attribute named catalog that is a Pandas DataFrame provinding the catalog of experimental data as shown below.
The columns formula and tabulated indicate the type of data. If n or k is included in the column tamulated, the experimentally observed refractive index n or extinction coefficient k is gi... | ri.catalog.head(3) | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
The experimental data are given by raw_data: | ri.raw_data.loc[3].head(5) # first 5 rows for the material whose id is 3 | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
where n is the refractive index and k is the extinction coefficient at the vacuum wavelength wl_n (wl_k) in the unit of μm.
The column c gives the coefficients for the dielectric function model.
In the above example, no coefficient is given because only the tabulated data are given (formula number in catalog is 0).
... | ri.catalog.tail(3)
ri.raw_data.loc[2911].head(5) # first 5 rows for the material whose id is 2912 | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
Using the method load_grid_data(), you can get grid data calculated at 200 wavelength values in the range [wl_min, wl_max], which is the intersection between the domain of n [wl_n_min, wl_n_max] and the domain of k [wl_k_min, wl_k_max]. These values are shown in catalog. | grid_data = ri.load_grid_data(3)
grid_data | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
Helper Methods
By using the functionality of Pandas, you may find what you want, easily. But, here some simple helper methods are implemented.
plot
plot(id: int, comp: str = "n", fmt1: Optional[str] = "-", fmt2: Optional[str] = "--", **kwargs)
* id (int): ID number.
* comp (str): 'n', 'k' or 'eps'.
* fmt1 (Union[str, N... | import matplotlib.pyplot as plt
ri.plot(3, "n")
plt.show()
ri.plot(3, "k")
plt.show()
ri.plot(3, "eps")
plt.show() | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
search
search(name: str) -> DataFrame
This method searches data whose book or book_name contain given name and return a simplified catalog for them. | ri.search("NaCl")
ri.search("sodium") # upper or lower case is not significant | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
select
select(condition: str) -> DataFrame
This method make a query with the given condition and return a simplified catalog. It will pick up materials whose experimental data contains some data that fulfill given condition. | ri.select("2.5 < n < 3 and 0.4 < wl < 0.8").head(10)
ri.plot(157) | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
show
show(id: int | Sequence[int]) -> DataFrame
This method shows a simplified catalog for given id. | ri.show(1) | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
read
read(id, as_dict=False)
This method returns the contants of a page associated with the id.
If you want the page contents as a python dict, give True for argument as_dict. | print(ri.read(0))
ri.read(0, as_dict=True) | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
references
references(id: int)
This method returns the REFERENCES of a page associated with the id. | ri.references(20) | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
material
material(params: dict) -> Material
Create Material-class instance for given parameter dict params.
params can includes the following parameters,
* 'id': ID number. (int)
* 'book': book value in catalog of RiiDataFrame. (str)
* 'page': page value in catalog of RiiDataFrame. (str)
* 'RI': Constant refractive ... | water = ri.material({'id': 428})
water.catalog | docs/notebooks/02_RiiDataFrame.ipynb | mnishida/RII_Pandas | mit |
Python depends on packages for most of its functionality; these can be either built-in (such as sys), or third-party (like all the packages below). Either way you need to import the packages you need before using them.
The Notebook
Look up http:/www.google.com Lets eat a burrito. $\alpha = \frac{\beta}{\gamma}$
Longer:... | df=pd.read_csv("all.csv", header=None,
names=["rating", 'review_count', 'isbn', 'booktype','author_url', 'year', 'genre_urls', 'dir','rating_count', 'name'],
)
df.head() | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
Notice we have a table! A spreadsheet! And it indexed the rows. Pandas (borrowing from R) calls it a DataFrame. Lets see the types of the columns...
df, in python parlance, is an instance of the pd.DataFrame class, created by calling the pd.read_csv function, which cllas the DataFrame constructor inside of it. If you d... | df.dtypes | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
This gives us Trues and Falses. Such a series is called a mask. If we count the number of Trues, and divide by the total, we'll get the fraction of ratings $\lt$ 3. To do this numerically see this: | np.sum(df.rating < 3) | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
Notice that you could just find the average since the Trues map to 1s. | np.mean(df.rating < 3.0) | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
Oppos we got an error. Something is not right. Its trying to convert some python datatype: None into an int. This usually means data was missing. Was it? | df[df.year.isnull()] | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
Much cleaner now!
Visualizing
Pandas has handy built in visualization. | df.rating.hist(); | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
We can do this in more detail, plotting against a mean, with cutom binsize or number of bins. Note how to label axes and create legends. | sns.set_context("notebook")
meanrat=df.rating.mean()
#you can get means and medians in different ways
print meanrat, np.mean(df.rating), df.rating.median()
with sns.axes_style("whitegrid"):
df.rating.hist(bins=30, alpha=0.4);
plt.axvline(meanrat, 0, 0.75, color='r', label='Mean')
plt.xlabel("average rating ... | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
One can see the sparseness of review counts. This will be important when we learn about recommendations: we'll have to regularize our models to deal with it. | df.review_count.hist(bins=np.arange(0, 40000, 400)) | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
Here we make a scatterplot in matplotlib of rating against year. By setting the alpha transparency low we can how the density of highly rated books on goodreads has changed. | plt.scatter(df.year, df.rating, lw=0, alpha=.08)
plt.xlim([1900,2010])
plt.xlabel("Year")
plt.ylabel("Rating") | Lab1-pythonpandas_original.ipynb | stevenydc/2015lab1 | mit |
In the cell below
I have calculated in the previous cell the loglikelyhood score of the partitioned sampling and vanilla sampling technique image-wise. So I have a score for each image. I have done this for all the MNIST digits that have been 'corrupted' by the bar images. That is RBM's trained models 1 - 9 and an RBM ... | for key in results:
logging.info("Plotting, win, lose and tie images for the {}".format(key))
results[key].plot_various_images() | Max/MNIST-ORBM-Inference.ipynb | garibaldu/multicauseRBM | mit |
Thoughts
So on a dataset of size 50, with 100 gibbs alterations we see in all cases that for the digit model, 1,2,3,..,9 that the partitioned sampling technique does either better or the same more often than the vanilla does. Let's try some different configurations. | results.update(results_for_models(models, corruption_model_name, 400, 500)) | Max/MNIST-ORBM-Inference.ipynb | garibaldu/multicauseRBM | mit |
results.update(results_for_models(models, corruption_model_name, 10, 1)) | results
# with open('results_dict', 'wb') as f3le:
# pickle.dump(results,f3le, protocol = None)
with open('results_dict', 'rb') as f4le:
results = pickle.load(f4le)
# for key in results:
# if key.startswith('400'):
# logging.info("Results for hiddens")
# r = results[key].stored_hiddens
# ... | Max/MNIST-ORBM-Inference.ipynb | garibaldu/multicauseRBM | mit |
1b. KNN (K=5)
cf. Machine Learning with Scikit-Learn | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X,y)
y_pred=knn.predict(X)
# compute classification accuracy for the logistic regression model
from sklearn import metrics
print(metrics.accuracy_score(y,y_pred))
### 1c. KNN (K=1)
knn = KNeighborsClassifier(n_neigh... | coursera_Ng/KNN.ipynb | ernestyalumni/MLgrabbag | mit |
2. Evaluation procedure 2 - Train/test split
Data is randomly assigned unless you use random_state hyperparameter
* If you use random_state=4
- Your data will be split exactly the same way | # STEP 1: split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4,random_state=4)
knn=KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train,y_train)
y_pred=knn.predict(X_test)
print(metrics.accuracy_score(y_test,y... | coursera_Ng/KNN.ipynb | ernestyalumni/MLgrabbag | mit |
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