diff preprocessors.py @ 8:1a9d5a8fff12 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/preprocessors.py	Tue May 14 18:03:50 2019 -0400
@@ -0,0 +1,184 @@
+"""
+Z_RandomOverSampler
+"""
+
+import imblearn
+import numpy as np
+
+from collections import Counter
+from imblearn.over_sampling.base import BaseOverSampler
+from imblearn.over_sampling import RandomOverSampler
+from imblearn.pipeline import Pipeline as imbPipeline
+from imblearn.utils import check_target_type
+from scipy import sparse
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing.data import _handle_zeros_in_scale
+from sklearn.utils import check_array, safe_indexing
+from sklearn.utils.fixes import nanpercentile
+from sklearn.utils.validation import (check_is_fitted, check_X_y,
+                                      FLOAT_DTYPES)
+
+
+class Z_RandomOverSampler(BaseOverSampler):
+
+    def __init__(self, sampling_strategy='auto',
+                 return_indices=False,
+                 random_state=None,
+                 ratio=None,
+                 negative_thres=0,
+                 positive_thres=-1):
+        super(Z_RandomOverSampler, self).__init__(
+            sampling_strategy=sampling_strategy, ratio=ratio)
+        self.random_state = random_state
+        self.return_indices = return_indices
+        self.negative_thres = negative_thres
+        self.positive_thres = positive_thres
+
+    @staticmethod
+    def _check_X_y(X, y):
+        y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'], dtype=None)
+        return X, y, binarize_y
+
+    def _fit_resample(self, X, y):
+        n_samples = X.shape[0]
+
+        # convert y to z_score
+        y_z = (y - y.mean()) / y.std()
+
+        index0 = np.arange(n_samples)
+        index_negative = index0[y_z > self.negative_thres]
+        index_positive = index0[y_z <= self.positive_thres]
+        index_unclassified = [x for x in index0
+                              if x not in index_negative
+                              and x not in index_positive]
+
+        y_z[index_negative] = 0
+        y_z[index_positive] = 1
+        y_z[index_unclassified] = -1
+
+        ros = RandomOverSampler(
+            sampling_strategy=self.sampling_strategy,
+            random_state=self.random_state,
+            ratio=self.ratio)
+        _, _ = ros.fit_resample(X, y_z)
+        sample_indices = ros.sample_indices_
+
+        print("Before sampler: %s. Total after: %s"
+              % (Counter(y_z), sample_indices.shape))
+
+        self.sample_indices_ = np.array(sample_indices)
+
+        if self.return_indices:
+            return (safe_indexing(X, sample_indices),
+                    safe_indexing(y, sample_indices),
+                    sample_indices)
+        return (safe_indexing(X, sample_indices),
+                safe_indexing(y, sample_indices))
+
+
+def _get_quantiles(X, quantile_range):
+    """
+    Calculate column percentiles for 2d array
+
+    Parameters
+    ----------
+    X : array-like, shape [n_samples, n_features]
+    """
+    quantiles = []
+    for feature_idx in range(X.shape[1]):
+        if sparse.issparse(X):
+            column_nnz_data = X.data[
+                X.indptr[feature_idx]: X.indptr[feature_idx + 1]]
+            column_data = np.zeros(shape=X.shape[0], dtype=X.dtype)
+            column_data[:len(column_nnz_data)] = column_nnz_data
+        else:
+            column_data = X[:, feature_idx]
+        quantiles.append(nanpercentile(column_data, quantile_range))
+
+    quantiles = np.transpose(quantiles)
+
+    return quantiles
+
+
+class TDMScaler(BaseEstimator, TransformerMixin):
+    """
+    Scale features using Training Distribution Matching (TDM) algorithm
+
+    References
+    ----------
+    .. [1] Thompson JA, Tan J and Greene CS (2016) Cross-platform
+           normalization of microarray and RNA-seq data for machine
+           learning applications. PeerJ 4, e1621.
+    """
+
+    def __init__(self, q_lower=25.0, q_upper=75.0, ):
+        self.q_lower = q_lower
+        self.q_upper = q_upper
+
+    def fit(self, X, y=None):
+        """
+        Parameters
+        ----------
+        X : array-like, shape [n_samples, n_features]
+        """
+        X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES,
+                        force_all_finite=True)
+
+        if not 0 <= self.q_lower <= self.q_upper <= 100:
+            raise ValueError("Invalid quantile parameter values: "
+                             "q_lower %s, q_upper: %s"
+                             % (str(self.q_lower), str(self.q_upper)))
+
+        # TODO sparse data
+        quantiles = nanpercentile(X, (self.q_lower, self.q_upper))
+        iqr = quantiles[1] - quantiles[0]
+
+        self.q_lower_ = quantiles[0]
+        self.q_upper_ = quantiles[1]
+        self.iqr_ = _handle_zeros_in_scale(iqr, copy=False)
+
+        self.max_ = np.nanmax(X)
+        self.min_ = np.nanmin(X)
+
+        return self
+
+    def transform(self, X):
+        """
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}
+            The data used to scale along the specified axis.
+        """
+        check_is_fitted(self, 'iqr_', 'max_')
+        X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES,
+                        force_all_finite=True)
+
+        # TODO sparse data
+        train_upper_scale = (self.max_ - self.q_upper_) / self.iqr_
+        train_lower_scale = (self.q_lower_ - self.min_) / self.iqr_
+
+        test_quantiles = nanpercentile(X, (self.q_lower, self.q_upper))
+        test_iqr = _handle_zeros_in_scale(
+            test_quantiles[1] - test_quantiles[0], copy=False)
+
+        test_upper_bound = test_quantiles[1] + train_upper_scale * test_iqr
+        test_lower_bound = test_quantiles[0] - train_lower_scale * test_iqr
+
+        test_min = np.nanmin(X)
+        if test_lower_bound < test_min:
+            test_lower_bound = test_min
+
+        X[X > test_upper_bound] = test_upper_bound
+        X[X < test_lower_bound] = test_lower_bound
+
+        X = (X - test_lower_bound) / (test_upper_bound - test_lower_bound)\
+            * (self.max_ - self.min_) + self.min_
+
+        return X
+
+    def inverse_transform(self, X):
+        """
+        Scale the data back to the original state
+        """
+        raise NotImplementedError("Inverse transformation is not implemented!")