Coverage for biobb_ml/classification/k_neighbors.py: 83%
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« prev ^ index » next coverage.py v7.5.1, created at 2024-05-07 09:39 +0000
1#!/usr/bin/env python3
3"""Module containing the KNeighborsTrain class and the command line interface."""
4import argparse
5import pandas as pd
6import numpy as np
7import joblib
8from biobb_common.generic.biobb_object import BiobbObject
9from sklearn.preprocessing import StandardScaler
10from sklearn.model_selection import train_test_split
11from sklearn.metrics import confusion_matrix, classification_report, log_loss
12from sklearn.neighbors import KNeighborsClassifier
13from biobb_common.configuration import settings
14from biobb_common.tools import file_utils as fu
15from biobb_common.tools.file_utils import launchlogger
16from biobb_ml.classification.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, getWeight, plotMultipleCM, plotBinaryClassifier
19class KNeighborsTrain(BiobbObject):
20 """
21 | biobb_ml KNeighborsTrain
22 | Wrapper of the scikit-learn KNeighborsClassifier method.
23 | Trains and tests a given dataset and saves the model and scaler. Visit the `KNeighborsClassifier documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html>`_ in the sklearn official website for further information.
25 Args:
26 input_dataset_path (str): Path to the input dataset. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/classification/dataset_k_neighbors.csv>`_. Accepted formats: csv (edam:format_3752).
27 output_model_path (str): Path to the output model file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_model_k_neighbors.pkl>`_. Accepted formats: pkl (edam:format_3653).
28 output_test_table_path (str) (Optional): Path to the test table file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_test_k_neighbors.csv>`_. Accepted formats: csv (edam:format_3752).
29 output_plot_path (str) (Optional): Path to the statistics plot. If target is binary it shows confusion matrix, distributions of the predicted probabilities of both classes and ROC curve. If target is non-binary it shows confusion matrix. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_plot_k_neighbors.png>`_. Accepted formats: png (edam:format_3603).
30 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
31 * **independent_vars** (*dict*) - ({}) Independent variables you want to train from your dataset. You can specify either a list of columns names from your input dataset, a list of columns indexes or a range of columns indexes. Formats: { "columns": ["column1", "column2"] } or { "indexes": [0, 2, 3, 10, 11, 17] } or { "range": [[0, 20], [50, 102]] }. In case of mulitple formats, the first one will be picked.
32 * **target** (*dict*) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { "column": "column3" } or { "index": 21 }. In case of mulitple formats, the first one will be picked.
33 * **weight** (*dict*) - ({}) Weight variable from your dataset. You can specify either a column name or a column index. Formats: { "column": "column3" } or { "index": 21 }. In case of mulitple formats, the first one will be picked.
34 * **metric** (*string*) - ("minkowski") The distance metric to use for the tree. Values: euclidean (Computes the Euclidean distance between two 1-D arrays), manhattan (Compute the Manhattan distance), chebyshev (Compute the Chebyshev distance), minkowski (Compute the Minkowski distance between two 1-D arrays), wminkowski (Compute the weighted Minkowski distance between two 1-D arrays), seuclidean (Return the standardized Euclidean distance between two 1-D arrays), mahalanobi (Compute the Mahalanobis distance between two 1-D arrays).
35 * **n_neighbors** (*int*) - (6) [1~100|1] Number of neighbors to use by default for kneighbors queries.
36 * **normalize_cm** (*bool*) - (False) Whether or not to normalize the confusion matrix.
37 * **random_state_train_test** (*int*) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split.
38 * **test_size** (*float*) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the test split. It should be between 0.0 and 1.0.
39 * **scale** (*bool*) - (False) Whether or not to scale the input dataset.
40 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
41 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
43 Examples:
44 This is a use example of how to use the building block from Python::
46 from biobb_ml.classification.k_neighbors import k_neighbors
47 prop = {
48 'independent_vars': {
49 'columns': [ 'column1', 'column2', 'column3' ]
50 },
51 'target': {
52 'column': 'target'
53 },
54 'n_neighbors': 6,
55 'test_size': 0.2
56 }
57 k_neighbors(input_dataset_path='/path/to/myDataset.csv',
58 output_model_path='/path/to/newModel.pkl',
59 output_test_table_path='/path/to/newTable.csv',
60 output_plot_path='/path/to/newPlot.png',
61 properties=prop)
63 Info:
64 * wrapped_software:
65 * name: scikit-learn KNeighborsClassifier
66 * version: >=0.24.2
67 * license: BSD 3-Clause
68 * ontology:
69 * name: EDAM
70 * schema: http://edamontology.org/EDAM.owl
72 """
74 def __init__(self, input_dataset_path, output_model_path,
75 output_test_table_path=None, output_plot_path=None, properties=None, **kwargs) -> None:
76 properties = properties or {}
78 # Call parent class constructor
79 super().__init__(properties)
80 self.locals_var_dict = locals().copy()
82 # Input/Output files
83 self.io_dict = {
84 "in": {"input_dataset_path": input_dataset_path},
85 "out": {"output_model_path": output_model_path, "output_test_table_path": output_test_table_path, "output_plot_path": output_plot_path}
86 }
88 # Properties specific for BB
89 self.independent_vars = properties.get('independent_vars', {})
90 self.target = properties.get('target', {})
91 self.weight = properties.get('weight', {})
92 self.metric = properties.get('metric', 'minkowski')
93 self.n_neighbors = properties.get('n_neighbors', 6)
94 self.normalize_cm = properties.get('normalize_cm', False)
95 self.random_state_train_test = properties.get('random_state_train_test', 5)
96 self.test_size = properties.get('test_size', 0.2)
97 self.scale = properties.get('scale', False)
98 self.properties = properties
100 # Check the properties
101 self.check_properties(properties)
102 self.check_arguments()
104 def check_data_params(self, out_log, err_log):
105 """ Checks all the input/output paths and parameters """
106 self.io_dict["in"]["input_dataset_path"] = check_input_path(self.io_dict["in"]["input_dataset_path"], "input_dataset_path", out_log, self.__class__.__name__)
107 self.io_dict["out"]["output_model_path"] = check_output_path(self.io_dict["out"]["output_model_path"], "output_model_path", False, out_log, self.__class__.__name__)
108 if self.io_dict["out"]["output_test_table_path"]:
109 self.io_dict["out"]["output_test_table_path"] = check_output_path(self.io_dict["out"]["output_test_table_path"], "output_test_table_path", True, out_log, self.__class__.__name__)
110 if self.io_dict["out"]["output_plot_path"]:
111 self.io_dict["out"]["output_plot_path"] = check_output_path(self.io_dict["out"]["output_plot_path"], "output_plot_path", True, out_log, self.__class__.__name__)
113 @launchlogger
114 def launch(self) -> int:
115 """Execute the :class:`KNeighborsTrain <classification.k_neighbors.KNeighborsTrain>` classification.k_neighbors.KNeighborsTrain object."""
117 # check input/output paths and parameters
118 self.check_data_params(self.out_log, self.err_log)
120 # Setup Biobb
121 if self.check_restart():
122 return 0
123 self.stage_files()
125 # load dataset
126 fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
127 if 'columns' in self.independent_vars:
128 labels = getHeader(self.io_dict["in"]["input_dataset_path"])
129 skiprows = 1
130 else:
131 labels = None
132 skiprows = None
133 data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
135 # declare inputs, targets and weights
136 # the inputs are all the independent variables
137 X = getIndependentVars(self.independent_vars, data, self.out_log, self.__class__.__name__)
138 fu.log('Independent variables: [%s]' % (getIndependentVarsList(self.independent_vars)), self.out_log, self.global_log)
139 # target
140 y = getTarget(self.target, data, self.out_log, self.__class__.__name__)
141 fu.log('Target: %s' % (getTargetValue(self.target)), self.out_log, self.global_log)
142 # weights
143 if self.weight:
144 w = getWeight(self.weight, data, self.out_log, self.__class__.__name__)
145 fu.log('Weight column provided', self.out_log, self.global_log)
147 # train / test split
148 fu.log('Creating train and test sets', self.out_log, self.global_log)
149 arrays_sets = (X, y)
150 # if user provide weights
151 if self.weight:
152 arrays_sets = arrays_sets + (w,)
153 X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state_train_test)
154 else:
155 X_train, X_test, y_train, y_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state_train_test)
157 # scale dataset
158 if self.scale:
159 fu.log('Scaling dataset', self.out_log, self.global_log)
160 scaler = StandardScaler()
161 X_train = scaler.fit_transform(X_train)
163 # classification
164 fu.log('Training dataset applying k neighbors classification', self.out_log, self.global_log)
165 model = KNeighborsClassifier(n_neighbors=self.n_neighbors)
167 arrays_fit = (X_train, y_train)
168 # if user provide weights
169 if self.weight:
170 arrays_fit = arrays_fit + (w_train,)
172 model.fit(*arrays_fit)
174 y_hat_train = model.predict(X_train)
175 # classification report
176 cr_train = classification_report(y_train, y_hat_train)
177 # log loss
178 yhat_prob_train = model.predict_proba(X_train)
179 l_loss_train = log_loss(y_train, yhat_prob_train)
180 fu.log('Calculating scores and report for training dataset\n\nCLASSIFICATION REPORT\n\n%s\nLog loss: %.3f\n' % (cr_train, l_loss_train), self.out_log, self.global_log)
182 # compute confusion matrix
183 cnf_matrix_train = confusion_matrix(y_train, y_hat_train)
184 np.set_printoptions(precision=2)
185 if self.normalize_cm:
186 cnf_matrix_train = cnf_matrix_train.astype('float') / cnf_matrix_train.sum(axis=1)[:, np.newaxis]
187 cm_type = 'NORMALIZED CONFUSION MATRIX'
188 else:
189 cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION'
191 fu.log('Calculating confusion matrix for training dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix_train), self.out_log, self.global_log)
193 # testing
194 # predict data from x_test
195 if self.scale:
196 X_test = scaler.transform(X_test)
197 y_hat_test = model.predict(X_test)
198 test_table = pd.DataFrame()
199 y_hat_prob = model.predict_proba(X_test)
200 y_hat_prob = np.around(y_hat_prob, decimals=2)
201 y_hat_prob = tuple(map(tuple, y_hat_prob))
202 test_table['P' + np.array2string(np.unique(y_test))] = y_hat_prob
203 y_test = y_test.reset_index(drop=True)
204 test_table['target'] = y_test
205 fu.log('Testing\n\nTEST DATA\n\n%s\n' % test_table, self.out_log, self.global_log)
207 # classification report
208 cr = classification_report(y_test, y_hat_test)
209 # log loss
210 yhat_prob = model.predict_proba(X_test)
211 l_loss = log_loss(y_test, yhat_prob)
212 fu.log('Calculating scores and report for testing dataset\n\nCLASSIFICATION REPORT\n\n%s\nLog loss: %.3f\n' % (cr, l_loss), self.out_log, self.global_log)
214 # compute confusion matrix
215 cnf_matrix = confusion_matrix(y_test, y_hat_test)
216 np.set_printoptions(precision=2)
217 if self.normalize_cm:
218 cnf_matrix = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis]
219 cm_type = 'NORMALIZED CONFUSION MATRIX'
220 else:
221 cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION'
223 fu.log('Calculating confusion matrix for testing dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix), self.out_log, self.global_log)
225 if (self.io_dict["out"]["output_test_table_path"]):
226 fu.log('Saving testing data to %s' % self.io_dict["out"]["output_test_table_path"], self.out_log, self.global_log)
227 test_table.to_csv(self.io_dict["out"]["output_test_table_path"], index=False, header=True)
229 # plot
230 if self.io_dict["out"]["output_plot_path"]:
231 vs = y.unique().tolist()
232 vs.sort()
233 if len(vs) > 2:
234 plot = plotMultipleCM(cnf_matrix_train, cnf_matrix, self.normalize_cm, vs)
235 fu.log('Saving confusion matrix plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
236 else:
237 plot = plotBinaryClassifier(model, yhat_prob_train, yhat_prob, cnf_matrix_train, cnf_matrix, y_train, y_test, normalize=self.normalize_cm)
238 fu.log('Saving binary classifier evaluator plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
239 plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
241 # save model, scaler and parameters
242 tv = y.unique().tolist()
243 tv.sort()
244 variables = {
245 'target': self.target,
246 'independent_vars': self.independent_vars,
247 'scale': self.scale,
248 'target_values': tv
249 }
250 fu.log('Saving model to %s' % self.io_dict["out"]["output_model_path"], self.out_log, self.global_log)
251 with open(self.io_dict["out"]["output_model_path"], "wb") as f:
252 joblib.dump(model, f)
253 if self.scale:
254 joblib.dump(scaler, f)
255 joblib.dump(variables, f)
257 # Copy files to host
258 self.copy_to_host()
260 self.tmp_files.extend([
261 self.stage_io_dict.get("unique_dir")
262 ])
263 self.remove_tmp_files()
265 self.check_arguments(output_files_created=True, raise_exception=False)
267 return 0
270def k_neighbors(input_dataset_path: str, output_model_path: str, output_test_table_path: str = None, output_plot_path: str = None, properties: dict = None, **kwargs) -> int:
271 """Execute the :class:`KNeighborsTrain <classification.k_neighbors.KNeighborsTrain>` class and
272 execute the :meth:`launch() <classification.k_neighbors.KNeighborsTrain.launch>` method."""
274 return KNeighborsTrain(input_dataset_path=input_dataset_path,
275 output_model_path=output_model_path,
276 output_test_table_path=output_test_table_path,
277 output_plot_path=output_plot_path,
278 properties=properties, **kwargs).launch()
281def main():
282 """Command line execution of this building block. Please check the command line documentation."""
283 parser = argparse.ArgumentParser(description="Wrapper of the scikit-learn KNeighborsClassifier method. ", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
284 parser.add_argument('--config', required=False, help='Configuration file')
286 # Specific args of each building block
287 required_args = parser.add_argument_group('required arguments')
288 required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
289 required_args.add_argument('--output_model_path', required=True, help='Path to the output model file. Accepted formats: pkl.')
290 parser.add_argument('--output_test_table_path', required=False, help='Path to the test table file. Accepted formats: csv.')
291 parser.add_argument('--output_plot_path', required=False, help='Path to the statistics plot. If target is binary it shows confusion matrix, distributions of the predicted probabilities of both classes and ROC curve. If target is non-binary it shows confusion matrix. Accepted formats: png.')
293 args = parser.parse_args()
294 args.config = args.config or "{}"
295 properties = settings.ConfReader(config=args.config).get_prop_dic()
297 # Specific call of each building block
298 k_neighbors(input_dataset_path=args.input_dataset_path,
299 output_model_path=args.output_model_path,
300 output_test_table_path=args.output_test_table_path,
301 output_plot_path=args.output_plot_path,
302 properties=properties)
305if __name__ == '__main__':
306 main()