From 43e3083ac2b9d154b5d3420307d6690b82800302 Mon Sep 17 00:00:00 2001 From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com> Date: Sun, 10 Nov 2019 01:38:39 -0600 Subject: [PATCH] analysis.py v 1.1.11.001 --- data analysis/analysis/analysis.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py index ad2e772f..3588fa3b 100644 --- a/data analysis/analysis/analysis.py +++ b/data analysis/analysis/analysis.py @@ -7,10 +7,12 @@ # current benchmark of optimization: 1.33 times faster # setup: -__version__ = "1.1.11.000" +__version__ = "1.1.11.001" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: + 1.1.11.001: + - added test/train split to RandomForestClassifier and RandomForestRegressor 1.1.11.000: - added RandomForestClassifier and RandomForestRegressor - note: untested @@ -554,17 +556,26 @@ class SVM: return r_2, _mse, _rms -def RandomForestClassifier(data, labels, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): +def RandomForestClassifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) - kernel.fit(data, labels) - return kernel + kernel.fit(data_train, labels_train) + predictions = kernel.predict(data_test) + cm = sklearn.metrics.confusion_matrix(predictions, predictions) + cr = sklearn.metrics.classification_report(predictions, predictions) + return kernel, cm, cr -def RandomForestRegressor(inputs, outputs, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): +def RandomForestRegressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(inputs, outputs, test_size=test_size, random_state=1) kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) - kernel.fit(inputs, outputs) - return kernel + kernel.fit(data_train, outputs_train) + predictions = kernel.predict(data_test) + r_2 = r_squared(predictions, outputs_test) + _mse = mse(predictions, outputs_test) + _rms = rms(predictions, outputs_test) + return kernel, r_2, _mse, _rms class Regression: