From f840863c7b7c7347cd81f4a3b6b5b4b551ada809 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 12:42:14 +0200 Subject: [PATCH 01/15] faster computational times using numba --- morfist/core.py | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index 98868f2..1515aeb 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -1,7 +1,17 @@ import numpy as np import scipy.stats import copy -from fast_histogram import histogram1d +from numba import njit + + +@njit +def hist1d(data, bins): + return np.histogram(data, bins)[0] + + +@njit +def bincount(data): + return np.bincount(data) # Class in charge of finding the best split at every given moment @@ -101,8 +111,10 @@ def __impurity_node(self, y): def impurity_classification(y_classification): # FIXME: this is one of the bottlenecks y_classification = y_classification.astype(int) - frequency = np.bincount(y_classification) / y_classification.size + + frequency = bincount(y_classification) / y_classification.size frequency = frequency[frequency != 0] + return 0 - np.array([f * np.log2(f) for f in frequency]).sum() # Calculate the impurity value for the regression task @@ -111,10 +123,12 @@ def impurity_regression(y_regression): return 0 n_bins = 100 - histogram = histogram1d(y, bins=n_bins, range=(y.min(), y.max())) - frequency = histogram / len(y) + bin_width = (y.max() - y.min()) / n_bins + + frequency = hist1d(y, bins=n_bins) + frequency = (frequency / len(y)) / bin_width + probability = (frequency + 1) / (frequency.sum() + n_bins) - bin_width = (y_regression.max() - y_regression.min()) / n_bins return 0 - bin_width * (probability * np.log2(probability)).sum() From a36112377a47737b590644aaf0dc9db0609bac54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 12:45:24 +0200 Subject: [PATCH 02/15] even faster classification --- morfist/core.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index 1515aeb..1cd6890 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -9,9 +9,15 @@ def hist1d(data, bins): return np.histogram(data, bins)[0] +# Calculate the impurity value for the classification task @njit -def bincount(data): - return np.bincount(data) +def impurity_classification(y_classification): + y_classification = y_classification.astype(np.int32) + + frequency = np.bincount(y_classification) / y_classification.size + frequency = frequency[frequency != 0] + + return 0 - np.array([f * np.log2(f) for f in frequency]).sum() # Class in charge of finding the best split at every given moment @@ -107,15 +113,6 @@ def __try_split(self, x, y, feature, value): # Calculate the impurity of a node def __impurity_node(self, y): - # Calculate the impurity value for the classification task - def impurity_classification(y_classification): - # FIXME: this is one of the bottlenecks - y_classification = y_classification.astype(int) - - frequency = bincount(y_classification) / y_classification.size - frequency = frequency[frequency != 0] - - return 0 - np.array([f * np.log2(f) for f in frequency]).sum() # Calculate the impurity value for the regression task def impurity_regression(y_regression): From ef994e1d765e71113b5c6be2b2e863819a13abc5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 13:28:08 +0200 Subject: [PATCH 03/15] faster regression --- morfist/core.py | 41 +++++++++++++++++++---------------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index 1cd6890..bd531d2 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -4,11 +4,6 @@ from numba import njit -@njit -def hist1d(data, bins): - return np.histogram(data, bins)[0] - - # Calculate the impurity value for the classification task @njit def impurity_classification(y_classification): @@ -20,6 +15,24 @@ def impurity_classification(y_classification): return 0 - np.array([f * np.log2(f) for f in frequency]).sum() +# Calculate the impurity value for the regression task +@njit +def impurity_regression(y, y_regression): + if np.unique(y_regression).size < 2: + return 0 + + n_bins = 100 + bin_width = (y.max() - y.min()) / n_bins + + frequency = np.histogram(y_regression, n_bins)[0] + frequency_float = frequency.astype(np.float64) + frequency_float = (frequency_float / len(y)) / bin_width + + probability = (frequency_float + 1) / (frequency_float.sum() + n_bins) + + return 0 - bin_width * (probability * np.log2(probability)).sum() + + # Class in charge of finding the best split at every given moment # Parameters: # x: training data @@ -113,22 +126,6 @@ def __try_split(self, x, y, feature, value): # Calculate the impurity of a node def __impurity_node(self, y): - - # Calculate the impurity value for the regression task - def impurity_regression(y_regression): - if np.unique(y_regression).size < 2: - return 0 - - n_bins = 100 - bin_width = (y.max() - y.min()) / n_bins - - frequency = hist1d(y, bins=n_bins) - frequency = (frequency / len(y)) / bin_width - - probability = (frequency + 1) / (frequency.sum() + n_bins) - - return 0 - bin_width * (probability * np.log2(probability)).sum() - delta = 0.0001 impurity = np.zeros(self.n_targets) # Calculate the impurity value for each of the targets(classification or regression) @@ -136,7 +133,7 @@ def impurity_regression(y_regression): if i in self.classification_targets: impurity[i] = impurity_classification(y[:, i]) + delta else: - impurity[i] = impurity_regression(y[:, i]) + delta + impurity[i] = impurity_regression(y, y[:, i]) + delta return impurity # Calculate the impurity of a split From 2b18f40a67c3859e0afacfd0f086ee44891e8cb0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 14:14:50 +0200 Subject: [PATCH 04/15] faster tasks --- morfist/core.py | 36 ++++++++++++++++++++++++++---------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index bd531d2..5f77563 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -33,6 +33,22 @@ def impurity_regression(y, y_regression): return 0 - bin_width * (probability * np.log2(probability)).sum() +@njit +def unique(x, feature): + return np.unique(x[:, feature]) + + +@njit +def get_gain(imp_n_left, imp_n_right, imp_n, imp_root, n_left, n_right, n_parent): + impurity_left = imp_n_left / imp_root + impurity_right = imp_n_right / imp_root + impurity_parent = imp_n / imp_root + + gain_left = (n_left / n_parent) * (impurity_parent - impurity_left) + gain_right = (n_right / n_parent) * (impurity_parent - impurity_right) + return gain_left + gain_right + + # Class in charge of finding the best split at every given moment # Parameters: # x: training data @@ -93,7 +109,7 @@ def __find_best_split(self, x, y): # Try each of the selected features and find which of them gives the best split(higher impurity) for feature in try_features: # Get the unique possible values for this particular feature - values = np.unique(x[:, feature]) + values = unique(x, feature) # We ensure that there are at least 2 different values if values.size < 2: @@ -138,20 +154,20 @@ def __impurity_node(self, y): # Calculate the impurity of a split def __impurity_split(self, y, y_left, y_right): - n_parent = y.shape[0] n_left = y_left.shape[0] n_right = y_right.shape[0] - if n_left < self.min_samples_leaf or n_right < self.min_samples_leaf: return np.inf else: - impurity_left = self.__impurity_node(y_left) / self.root_impurity - impurity_right = self.__impurity_node(y_right) / self.root_impurity - impurity_parent = self.__impurity_node(y) / self.root_impurity - - gain_left = (n_left / n_parent) * (impurity_parent - impurity_left) - gain_right = (n_right / n_parent) * (impurity_parent - impurity_right) - gain = gain_left + gain_right + n_parent = y.shape[0] + + gain = get_gain(self.__impurity_node(y_left), + self.__impurity_node(y_right), + self.__impurity_node(y), + self.root_impurity, + n_left, + n_right, + n_parent) if self.choose_split == 'mean': return gain.mean() From 3a5ba42a4afb232f84df37c0f8d7550c27c3c648 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 16:21:13 +0200 Subject: [PATCH 05/15] faster classification --- morfist/core.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index 5f77563..72fe42b 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -7,12 +7,13 @@ # Calculate the impurity value for the classification task @njit def impurity_classification(y_classification): - y_classification = y_classification.astype(np.int32) + frequency = np.bincount(y_classification.astype(np.int16)) / y_classification.size - frequency = np.bincount(y_classification) / y_classification.size - frequency = frequency[frequency != 0] + result = 0 + for f in frequency: + result += f * np.log2(f) - return 0 - np.array([f * np.log2(f) for f in frequency]).sum() + return 0 - result # Calculate the impurity value for the regression task From 14050cd780e0d8f8fa2eec818ff2849a72c75532 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 17:11:22 +0200 Subject: [PATCH 06/15] fix: incorrect results --- morfist/core.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/morfist/core.py b/morfist/core.py index 72fe42b..69faba2 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -7,7 +7,11 @@ # Calculate the impurity value for the classification task @njit def impurity_classification(y_classification): - frequency = np.bincount(y_classification.astype(np.int16)) / y_classification.size + y_class = y_classification.astype(np.int16) + + # Calculate frequencies + frequency = np.bincount(y_class) / y_classification.size + frequency = frequency[frequency != 0] result = 0 for f in frequency: From fb6af115fc0b80a225a5f46496ca6489c14d4d68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Wed, 8 Apr 2020 17:44:28 +0200 Subject: [PATCH 07/15] refacto --- morfist/core.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/morfist/core.py b/morfist/core.py index 69faba2..e4ca9ee 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -7,10 +7,14 @@ # Calculate the impurity value for the classification task @njit def impurity_classification(y_classification): + # Cast to integer y_class = y_classification.astype(np.int16) # Calculate frequencies - frequency = np.bincount(y_class) / y_classification.size + bincount = np.bincount(y_class) + frequency = bincount / y_class.size + + # Remove 0 values frequency = frequency[frequency != 0] result = 0 From 3ef6265150f57687629056bb3f7c3c84042bf938 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Thu, 9 Apr 2020 21:31:34 +0200 Subject: [PATCH 08/15] speed improvement --- morfist/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index e4ca9ee..df0b580 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -18,8 +18,8 @@ def impurity_classification(y_classification): frequency = frequency[frequency != 0] result = 0 - for f in frequency: - result += f * np.log2(f) + for i in range(frequency.size): + result += frequency[i] * np.log2(frequency[i]) return 0 - result From b6cf13df7128c7fe76ab591acc3b216d4640a55b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Fri, 10 Apr 2020 18:21:57 +0200 Subject: [PATCH 09/15] adjust value of min_sample_leaf --- test/test.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test.py b/test/test.py index d3447db..9291ea2 100644 --- a/test/test.py +++ b/test/test.py @@ -67,7 +67,7 @@ def test_reg(): t1_start = perf_counter() reg_morfist = MixedRandomForest( n_estimators=n_trees, - min_samples_leaf=5 + min_samples_leaf=1 ) # Calculate morfist scores using cross-validation @@ -110,7 +110,7 @@ def test_mix_1(): mix_rf = MixedRandomForest( n_estimators=n_trees, - min_samples_leaf=5, + min_samples_leaf=1, classification_targets=[1] ) @@ -136,7 +136,7 @@ def test_mix_2(): mix_rf = MixedRandomForest( n_estimators=n_trees, - min_samples_leaf=5, + min_samples_leaf=1, classification_targets=[0] ) From c85e4f785dd85cd9b16946dda2741e65e2e1f2b3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Fri, 10 Apr 2020 18:56:39 +0200 Subject: [PATCH 10/15] refacto --- morfist/core.py | 55 ++++++++++++++++++++++++------------------------- 1 file changed, 27 insertions(+), 28 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index df0b580..05cde52 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -125,54 +125,41 @@ def __find_best_split(self, x, y): continue # Random value sub-sampling + # Reduces the size by one element + # This is to avoid using the first value in case it is 0 for regression + # [0] -> ([0] + [1]) / 2 values = (values[:-1] + values[1:]) / 2 + + # Choose a random amount of values, with a min of 2 values = np.random.choice(values, min(2, values.size)) # Try to split with this specific combination of feature and values + # Here lies the computational burden, as we try every possible split + # TODO incrementally compute impurity for value in values: - impurity = self.__try_split(x, y, feature, value) + + left_idx = x[:, feature] <= value + right_idx = x[:, feature] > value + + impurity = self.__impurity_split(y, y[left_idx, :], y[right_idx, :]) # If it's better than the previous saved one, save the values if impurity > best_impurity: best_feature, best_value, best_impurity = feature, value, impurity return best_feature, best_value, best_impurity - # Try a specific split - # Parameters - # x: x data - # y: y data - # f: feature - # t: value - def __try_split(self, x, y, feature, value): - left_idx = x[:, feature] <= value - right_idx = x[:, feature] > value - - return self.__impurity_split(y, y[left_idx, :], y[right_idx, :]) - - # Calculate the impurity of a node - def __impurity_node(self, y): - delta = 0.0001 - impurity = np.zeros(self.n_targets) - # Calculate the impurity value for each of the targets(classification or regression) - for i in range(self.n_targets): - if i in self.classification_targets: - impurity[i] = impurity_classification(y[:, i]) + delta - else: - impurity[i] = impurity_regression(y, y[:, i]) + delta - return impurity - # Calculate the impurity of a split - def __impurity_split(self, y, y_left, y_right): + def __impurity_split(self, y_parent, y_left, y_right): n_left = y_left.shape[0] n_right = y_right.shape[0] if n_left < self.min_samples_leaf or n_right < self.min_samples_leaf: return np.inf else: - n_parent = y.shape[0] + n_parent = y_parent.shape[0] gain = get_gain(self.__impurity_node(y_left), self.__impurity_node(y_right), - self.__impurity_node(y), + self.__impurity_node(y_parent), self.root_impurity, n_left, n_right, @@ -183,6 +170,18 @@ def __impurity_split(self, y, y_left, y_right): else: return gain.max() + # Calculate the impurity of a node + def __impurity_node(self, y): + delta = 0.0001 + impurity = np.zeros(self.n_targets) + # Calculate the impurity value for each of the targets(classification or regression) + for i in range(self.n_targets): + if i in self.classification_targets: + impurity[i] = impurity_classification(y[:, i]) + delta + else: + impurity[i] = impurity_regression(y, y[:, i]) + delta + return impurity + # Build a Random Tree # Parameters: From e6e6b6111e23cb4e431ee96e7c951199b743176a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Sat, 11 Apr 2020 22:22:54 +0200 Subject: [PATCH 11/15] optimize if statement --- morfist/core.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index 05cde52..eb59d10 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -243,16 +243,15 @@ def fit(self, x, y): if feature: left_children.append(i + len(split_queue) + 1) right_children.append(i + len(split_queue) + 2) - else: - left_children.append(None) - right_children.append(None) - if feature: l_idx = next_x[:, feature] <= value r_idx = next_x[:, feature] > value split_queue.append((next_x[l_idx, :], next_y[l_idx, :])) split_queue.append((next_x[r_idx, :], next_y[r_idx, :])) + else: + left_children.append(None) + right_children.append(None) i += 1 From b313975516b69e4ffd52ea82950d8692f796c22d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Mon, 13 Apr 2020 11:39:37 +0200 Subject: [PATCH 12/15] optimization --- morfist/core.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/morfist/core.py b/morfist/core.py index eb59d10..7c1e064 100644 --- a/morfist/core.py +++ b/morfist/core.py @@ -11,15 +11,12 @@ def impurity_classification(y_classification): y_class = y_classification.astype(np.int16) # Calculate frequencies - bincount = np.bincount(y_class) - frequency = bincount / y_class.size - - # Remove 0 values - frequency = frequency[frequency != 0] + frequency = np.bincount(y_class) / y_class.size result = 0 for i in range(frequency.size): - result += frequency[i] * np.log2(frequency[i]) + if frequency[i]: + result += frequency[i] * np.log2(frequency[i]) return 0 - result From 553cc4626787f2c23052b051af57678d24893bd9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9s=20Rever=C3=B3n=20Molina?= Date: Sun, 26 Apr 2020 12:12:12 +0200 Subject: [PATCH 13/15] make package available on pypy and anaconda --- README.md | 12 ++++++++++++ setup.py | 28 ++++++++++++++++++++++++++++ test.cprof | Bin 469683 -> 0 bytes test_new.cprof | Bin 469325 -> 0 bytes time_profile.png | Bin 116063 -> 0 bytes time_profile_new.png | Bin 108812 -> 0 bytes 6 files changed, 40 insertions(+) create mode 100644 setup.py delete mode 100644 test.cprof delete mode 100644 test_new.cprof delete mode 100644 time_profile.png delete mode 100644 time_profile_new.png diff --git a/README.md b/README.md index 914c63c..46def96 100644 --- a/README.md +++ b/README.md @@ -8,12 +8,24 @@ of classification tasks and regression tasks, simultaneously. Morfist's mixed mu * [Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32](https://link.springer.com/article/10.1023%2FA%3A1010933404324). * [Linusson, H. (2013). Multi-output random forests](https://pdfs.semanticscholar.org/4219/f87ed41c558d43cf78f63976cf87bcd7ebb0.pdf). +## Installation + +With pip: +``` +pip install decision-tree-morfist +``` +With conda: +``` +conda install -c systemallica decision-tree-morfist +``` ## Usage ### Initialising the model - Similarly to a scikit-learn [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), a MixedRandomForest can be initialised in this way: ``` +from morfist import MixedRandomForest + mrf = MixedRandomForest( n_estimators=n_trees, min_samples_leaf=1, diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..bf6fa92 --- /dev/null +++ b/setup.py @@ -0,0 +1,28 @@ +import pathlib +from setuptools import setup + +# The directory containing this file +HERE = pathlib.Path(__file__).parent + +# The text of the README file +README = (HERE / "README.md").read_text() + +# This call to setup() does all the work +setup( + name="decision-tree-morfist", + version="0.1.0", + description="Multi-target Random Forest implementation that can mix both classification and regression tasks.", + long_description=README, + long_description_content_type="text/markdown", + url="https://github.com/systemallica/morfist", + author="Andrés Reverón Molina", + author_email="andres@reveronmolina.me", + license="MIT", + classifiers=[ + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + ], + packages=["morfist"], + install_requires=["numpy", "numba", "scipy"], +) diff --git a/test.cprof b/test.cprof deleted file mode 100644 index 7781e1e196b0345f03358f0945ef75535371d0b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 469683 zcmcFs2b`Qm*$zI&GtWG2 zo_TM^@HOk5T^)v>H9OAl$(6f{*{RdJ;zD-%)MD4v=^g#$Ttah@Q;SoJ1y8ze*&FN%aB6>hInR zpdFb~ZjX5YwmKXitZXp9rua=Qu2)?i1d*^!%5m 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