#!/usr/bin/env python3
"""Module containing the KNeighborsTrain class and the command line interface."""
import argparse
import pandas as pd
import numpy as np
import joblib
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, log_loss
from sklearn.neighbors import KNeighborsClassifier
from biobb_common.configuration import settings
from biobb_common.tools import file_utils as fu
from biobb_common.tools.file_utils import launchlogger
from biobb_ml.classification.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, getWeight, plotMultipleCM, plotBinaryClassifier
[docs]class KNeighborsTrain(BiobbObject):
"""
| biobb_ml KNeighborsTrain
| Wrapper of the scikit-learn KNeighborsClassifier method.
| 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.
Args:
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).
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).
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).
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).
properties (dic - Python dictionary object containing the tool parameters, not input/output files):
* **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.
* **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.
* **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.
* **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).
* **n_neighbors** (*int*) - (6) [1~100|1] Number of neighbors to use by default for kneighbors queries.
* **normalize_cm** (*bool*) - (False) Whether or not to normalize the confusion matrix.
* **random_state_train_test** (*int*) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split.
* **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.
* **scale** (*bool*) - (False) Whether or not to scale the input dataset.
* **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
* **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
Examples:
This is a use example of how to use the building block from Python::
from biobb_ml.classification.k_neighbors import k_neighbors
prop = {
'independent_vars': {
'columns': [ 'column1', 'column2', 'column3' ]
},
'target': {
'column': 'target'
},
'n_neighbors': 6,
'test_size': 0.2
}
k_neighbors(input_dataset_path='/path/to/myDataset.csv',
output_model_path='/path/to/newModel.pkl',
output_test_table_path='/path/to/newTable.csv',
output_plot_path='/path/to/newPlot.png',
properties=prop)
Info:
* wrapped_software:
* name: scikit-learn KNeighborsClassifier
* version: >=0.24.2
* license: BSD 3-Clause
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_dataset_path, output_model_path,
output_test_table_path=None, output_plot_path=None, properties=None, **kwargs) -> None:
properties = properties or {}
# Call parent class constructor
super().__init__(properties)
self.locals_var_dict = locals().copy()
# Input/Output files
self.io_dict = {
"in": {"input_dataset_path": input_dataset_path},
"out": {"output_model_path": output_model_path, "output_test_table_path": output_test_table_path, "output_plot_path": output_plot_path}
}
# Properties specific for BB
self.independent_vars = properties.get('independent_vars', {})
self.target = properties.get('target', {})
self.weight = properties.get('weight', {})
self.metric = properties.get('metric', 'minkowski')
self.n_neighbors = properties.get('n_neighbors', 6)
self.normalize_cm = properties.get('normalize_cm', False)
self.random_state_train_test = properties.get('random_state_train_test', 5)
self.test_size = properties.get('test_size', 0.2)
self.scale = properties.get('scale', False)
self.properties = properties
# Check the properties
self.check_properties(properties)
self.check_arguments()
[docs] def check_data_params(self, out_log, err_log):
""" Checks all the input/output paths and parameters """
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__)
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__)
if self.io_dict["out"]["output_test_table_path"]:
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__)
if self.io_dict["out"]["output_plot_path"]:
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__)
[docs] @launchlogger
def launch(self) -> int:
"""Execute the :class:`KNeighborsTrain <classification.k_neighbors.KNeighborsTrain>` classification.k_neighbors.KNeighborsTrain object."""
# check input/output paths and parameters
self.check_data_params(self.out_log, self.err_log)
# Setup Biobb
if self.check_restart():
return 0
self.stage_files()
# load dataset
fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
if 'columns' in self.independent_vars:
labels = getHeader(self.io_dict["in"]["input_dataset_path"])
skiprows = 1
else:
labels = None
skiprows = None
data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
# declare inputs, targets and weights
# the inputs are all the independent variables
X = getIndependentVars(self.independent_vars, data, self.out_log, self.__class__.__name__)
fu.log('Independent variables: [%s]' % (getIndependentVarsList(self.independent_vars)), self.out_log, self.global_log)
# target
y = getTarget(self.target, data, self.out_log, self.__class__.__name__)
fu.log('Target: %s' % (getTargetValue(self.target)), self.out_log, self.global_log)
# weights
if self.weight:
w = getWeight(self.weight, data, self.out_log, self.__class__.__name__)
fu.log('Weight column provided', self.out_log, self.global_log)
# train / test split
fu.log('Creating train and test sets', self.out_log, self.global_log)
arrays_sets = (X, y)
# if user provide weights
if self.weight:
arrays_sets = arrays_sets + (w,)
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)
else:
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)
# scale dataset
if self.scale:
fu.log('Scaling dataset', self.out_log, self.global_log)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
# classification
fu.log('Training dataset applying k neighbors classification', self.out_log, self.global_log)
model = KNeighborsClassifier(n_neighbors=self.n_neighbors)
arrays_fit = (X_train, y_train)
# if user provide weights
if self.weight:
arrays_fit = arrays_fit + (w_train,)
model.fit(*arrays_fit)
y_hat_train = model.predict(X_train)
# classification report
cr_train = classification_report(y_train, y_hat_train)
# log loss
yhat_prob_train = model.predict_proba(X_train)
l_loss_train = log_loss(y_train, yhat_prob_train)
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)
# compute confusion matrix
cnf_matrix_train = confusion_matrix(y_train, y_hat_train)
np.set_printoptions(precision=2)
if self.normalize_cm:
cnf_matrix_train = cnf_matrix_train.astype('float') / cnf_matrix_train.sum(axis=1)[:, np.newaxis]
cm_type = 'NORMALIZED CONFUSION MATRIX'
else:
cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION'
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)
# testing
# predict data from x_test
if self.scale:
X_test = scaler.transform(X_test)
y_hat_test = model.predict(X_test)
test_table = pd.DataFrame()
y_hat_prob = model.predict_proba(X_test)
y_hat_prob = np.around(y_hat_prob, decimals=2)
y_hat_prob = tuple(map(tuple, y_hat_prob))
test_table['P' + np.array2string(np.unique(y_test))] = y_hat_prob
y_test = y_test.reset_index(drop=True)
test_table['target'] = y_test
fu.log('Testing\n\nTEST DATA\n\n%s\n' % test_table, self.out_log, self.global_log)
# classification report
cr = classification_report(y_test, y_hat_test)
# log loss
yhat_prob = model.predict_proba(X_test)
l_loss = log_loss(y_test, yhat_prob)
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)
# compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_hat_test)
np.set_printoptions(precision=2)
if self.normalize_cm:
cnf_matrix = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis]
cm_type = 'NORMALIZED CONFUSION MATRIX'
else:
cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION'
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)
if (self.io_dict["out"]["output_test_table_path"]):
fu.log('Saving testing data to %s' % self.io_dict["out"]["output_test_table_path"], self.out_log, self.global_log)
test_table.to_csv(self.io_dict["out"]["output_test_table_path"], index=False, header=True)
# plot
if self.io_dict["out"]["output_plot_path"]:
vs = y.unique().tolist()
vs.sort()
if len(vs) > 2:
plot = plotMultipleCM(cnf_matrix_train, cnf_matrix, self.normalize_cm, vs)
fu.log('Saving confusion matrix plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
else:
plot = plotBinaryClassifier(model, yhat_prob_train, yhat_prob, cnf_matrix_train, cnf_matrix, y_train, y_test, normalize=self.normalize_cm)
fu.log('Saving binary classifier evaluator plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
# save model, scaler and parameters
tv = y.unique().tolist()
tv.sort()
variables = {
'target': self.target,
'independent_vars': self.independent_vars,
'scale': self.scale,
'target_values': tv
}
fu.log('Saving model to %s' % self.io_dict["out"]["output_model_path"], self.out_log, self.global_log)
with open(self.io_dict["out"]["output_model_path"], "wb") as f:
joblib.dump(model, f)
if self.scale:
joblib.dump(scaler, f)
joblib.dump(variables, f)
# Copy files to host
self.copy_to_host()
self.tmp_files.extend([
self.stage_io_dict.get("unique_dir")
])
self.remove_tmp_files()
self.check_arguments(output_files_created=True, raise_exception=False)
return 0
[docs]def 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:
"""Execute the :class:`KNeighborsTrain <classification.k_neighbors.KNeighborsTrain>` class and
execute the :meth:`launch() <classification.k_neighbors.KNeighborsTrain.launch>` method."""
return KNeighborsTrain(input_dataset_path=input_dataset_path,
output_model_path=output_model_path,
output_test_table_path=output_test_table_path,
output_plot_path=output_plot_path,
properties=properties, **kwargs).launch()
[docs]def main():
"""Command line execution of this building block. Please check the command line documentation."""
parser = argparse.ArgumentParser(description="Wrapper of the scikit-learn KNeighborsClassifier method. ", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
parser.add_argument('--config', required=False, help='Configuration file')
# Specific args of each building block
required_args = parser.add_argument_group('required arguments')
required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
required_args.add_argument('--output_model_path', required=True, help='Path to the output model file. Accepted formats: pkl.')
parser.add_argument('--output_test_table_path', required=False, help='Path to the test table file. Accepted formats: csv.')
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.')
args = parser.parse_args()
args.config = args.config or "{}"
properties = settings.ConfReader(config=args.config).get_prop_dic()
# Specific call of each building block
k_neighbors(input_dataset_path=args.input_dataset_path,
output_model_path=args.output_model_path,
output_test_table_path=args.output_test_table_path,
output_plot_path=args.output_plot_path,
properties=properties)
if __name__ == '__main__':
main()