From a50452dbfd0e9d109365e35b125152d7dd3808fd Mon Sep 17 00:00:00 2001 From: miaohancheng Date: Wed, 18 Dec 2024 11:38:29 +0800 Subject: [PATCH] Btree match --- Example.py | 4 +- Example_files/Example_14_0.png | Bin 45013 -> 45676 bytes Example_files/Example_15_0.png | Bin 29706 -> 45676 bytes Example_files/Example_16_0.png | Bin 20900 -> 28210 bytes Example_files/Example_17_0.png | Bin 20900 -> 28210 bytes Example_files/Example_19_0.png | Bin 14002 -> 28210 bytes Example_files/Example_Chinese_01_0.png | Bin 23897 -> 0 bytes Example_files/Example_Chinese_02_0.png | Bin 17105 -> 0 bytes build/lib/pysmatch/Matcher.py | 2 +- build/lib/pysmatch/__init__.py | 2 +- pysmatch/Matcher.py | 244 ++++++++++++++----------- pysmatch/__init__.py | 2 +- pysmatch/{functions.py => utils.py} | 0 13 files changed, 147 insertions(+), 107 deletions(-) delete mode 100644 Example_files/Example_Chinese_01_0.png delete mode 100644 Example_files/Example_Chinese_02_0.png rename pysmatch/{functions.py => utils.py} (100%) diff --git a/Example.py b/Example.py index 5429cd7..c74fee1 100644 --- a/Example.py +++ b/Example.py @@ -21,9 +21,9 @@ # for reproducibility np.random.seed(20240919) -m.fit_scores(balance=True, nmodels=10,n_jobs=3,model_type='knn') +# m.fit_scores(balance=True, nmodels=10,n_jobs=3,model_type='knn') # m.fit_scores(balance=True, nmodels=10, n_jobs=3, model_type='tree', max_iter=100) -# m.fit_scores(balance=True, nmodels=10,n_jobs=3,model_type='linear', max_iter=200) +m.fit_scores(balance=True, nmodels=10,n_jobs=3,model_type='linear', max_iter=200) m.predict_scores() diff --git a/Example_files/Example_14_0.png b/Example_files/Example_14_0.png index 4fb1b8f823cb49b59c94fecacd5c45f9fd5ba459..24bd396df95e99ea3776f034432103bc38e0376c 100644 GIT binary patch literal 45676 zcmeFZ^;=b47c~r`Vvr(Tih)Xpbg3X90*Z8pba#WIgrszX5+WUk?(Pyqx*HDN{m!HJ z`(Do<@csDh>pHjIxX<2euQlhGV~n{rU*1TGV&5RUfrf^LEiNW3gNBB&gocK`d+jRx zBY&RoyNUdu`sO;XFo!o=Lh#8CT=ovxL&p}83wBR3-}{T%}v8w+b5 zCMMJWcLJlil|Iv5DU4yb$aM=bC2KS^TrJcG{ik4>AsSl#u(>F|-!h=k&_)R*BAH=2q{E4)cQ3i*WMjXPen z-c5V_=Fa_SNh|jpW2ROlvOB)ZjJ4;qQ=7!=qtCx1s@=#A4gcTq_9G+B-$R>N_M6|o ze;-08T>AGz8atsY<^O$)^3DG{74!c;_5bS`2zV|ldt0X^@F}lXoTm~(FZk=%51E;C zM1G!QW2%UI!&UkTj#vGf>VxlzN>$_v5YW(I2i>s2558*B9`;y~J|ZI)&dydYnwfCk znD71mLa&8wV1I4YaUoh?OiC(eZ}EI@P=l1q;!a&%U1>!H0RcgSoPt_YmJ-6~Oi@n) zPT8kIDAGGPJBNP#T3fMEEg>Qz5|F@2N9UKBQ+|V!lM^n?Uwt@plaG(ji2H1qR~=0y zTfVQaPpVW

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zdNwZ+r}BYGwY1!;n*^5p(=6$3iS@jia2ct%MD_Oz}HCAq`?UmCZ}d5X(qDZedyp|!#1DK z$>!0%>7Q36>Jo}+G7zX>$n9P>a1ft4MdW~!>T4&qEuU1HG!KLNCeIHR$dPvQmci7# zv9Hzz3rT#GsUEtiqi30;UIM-Sf|k!zdRzHoCp#XCe&4_D$|y;gFN;{*c*F$7sBfhl zpkY!~T>*_+{tcO~cK2-jD3?OS&;{@}m06Cu^ZSHM&Au6h2>T3I&bM%no14r&9;rbjtEWcc`}9-4u4dUB%G;T5 zo*J}oo1^GV0@ImQ;R~Lvvb%S*>-VnA7u;{!I#8R-2}#xOhf9NFxKQ^a+rUy2Hsa+K z@QNpw4z4!;l5ylDG8Jg~*xpGwF8cu!i^|!1_PYgT?avZb@&^zr-#C#~?!wOAKF6Bn zsd4Hlcy9>ny*E%!@xG!kIuNx6hO87^3HLS4^^OMkc$_tExP>0{<5!jtbetcY`r&rcA6HS|H%_v~W+7k@rFMF0Q* diff --git a/build/lib/pysmatch/Matcher.py b/build/lib/pysmatch/Matcher.py index a74945c..ceb415a 100644 --- a/build/lib/pysmatch/Matcher.py +++ b/build/lib/pysmatch/Matcher.py @@ -4,7 +4,7 @@ from sklearn.preprocessing import StandardScaler from pysmatch import * -import pysmatch.functions as uf +import pysmatch.utils as uf from catboost import CatBoostClassifier from multiprocessing.pool import ThreadPool as Pool import multiprocessing as mp diff --git a/build/lib/pysmatch/__init__.py b/build/lib/pysmatch/__init__.py index 0b9de4b..95d88c5 100644 --- a/build/lib/pysmatch/__init__.py +++ b/build/lib/pysmatch/__init__.py @@ -6,7 +6,7 @@ from collections import Counter from itertools import chain import sys; sys.path.append(sys.argv[0]) -import pysmatch.functions as uf +import pysmatch.utils as uf import statsmodels.api as sm import patsy import seaborn as sns diff --git a/pysmatch/Matcher.py b/pysmatch/Matcher.py index 4c95dd3..3a67d58 100644 --- a/pysmatch/Matcher.py +++ b/pysmatch/Matcher.py @@ -1,16 +1,24 @@ from __future__ import print_function import matplotlib.pyplot as plt -from sklearn.neighbors import KNeighborsClassifier +from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors from sklearn.preprocessing import StandardScaler import logging from pysmatch import * -import pysmatch.functions as uf +import pysmatch.utils as uf from catboost import CatBoostClassifier +from multiprocessing import Pool from multiprocessing.pool import ThreadPool as Pool import multiprocessing as mp from sklearn.linear_model import LogisticRegression from imblearn.over_sampling import RandomOverSampler from sklearn.model_selection import train_test_split +import pandas as pd +import numpy as np +from typing import List, Optional +import patsy +import seaborn as sns +from statsmodels.distributions.empirical_distribution import ECDF +from scipy import stats logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', @@ -37,7 +45,8 @@ class Matcher: Useful for unique idenifiers """ - def __init__(self, test, control, yvar, formula=None, exclude=None): + def __init__(self, test: pd.DataFrame, control: pd.DataFrame, yvar: str, + formula: Optional[str] = None, exclude: Optional[List[str]] = None): if exclude is None: exclude = [] plt.rcParams["figure.figsize"] = (10, 5) @@ -81,7 +90,7 @@ def __init__(self, test, control, yvar, formula=None, exclude=None): logging.info(f'n majority:{len(self.data[self.data[yvar] == self.majority])}') logging.info(f'n minority:{len(self.data[self.data[yvar] == self.minority])}') - def preprocess_data(self, X, fit_scaler=False, index=None): + def preprocess_data(self, X: pd.DataFrame, fit_scaler: bool = False, index: Optional[int] = None) -> np.ndarray: X_encoded = pd.get_dummies(X) if not hasattr(self, 'X_columns'): @@ -101,7 +110,7 @@ def preprocess_data(self, X, fit_scaler=False, index=None): return X_scaled - def fit_model(self, index, X, y, model_type, balance, max_iter=100): + def fit_model(self, index: int, X: pd.DataFrame, y: pd.Series, model_type: str, balance: bool, max_iter: int = 100) -> dict: X_train, _, y_train, _ = train_test_split(X, y, train_size=0.7, random_state=index) if balance: @@ -137,7 +146,8 @@ def fit_model(self, index, X, y, model_type, balance, max_iter=100): logging.info(f"Model {index + 1}/{self.nmodels} trained. Accuracy: {accuracy:.2%}") return {'model': model, 'accuracy': accuracy} - def fit_scores(self, balance=True, nmodels=None, n_jobs=1, model_type='linear', max_iter=100): + def fit_scores(self, balance: bool = True, nmodels: Optional[int] = None, n_jobs: int = 1, + model_type: str = 'linear', max_iter: int = 100): self.models, self.model_accuracy = [], [] self.model_type = model_type num_cores = mp.cpu_count() @@ -196,7 +206,8 @@ def predict_scores(self): self.data['scores'] = scores - def match(self, threshold=0.001, nmatches=1, method='min', max_rand=10, replacement=False): + def match(self, threshold: float = 0.001, nmatches: int = 1, method: str = 'min', + max_rand: int = 10, replacement: bool = False): """ Finds suitable match(es) for each record in the minority dataset, if one exists. Records are excluded from the final @@ -232,45 +243,86 @@ def match(self, threshold=0.001, nmatches=1, method='min', max_rand=10, replacem self.fit_scores() # Fit the propensity score models self.predict_scores() # Predict propensity scores for the data - test_scores = self.data[self.data[self.yvar] == True][['scores']] # Get scores for the test group - ctrl_scores = self.data[self.data[self.yvar] == False][['scores']] # Get scores for the control group - result, match_ids = [], [] # Initialize the result list and match ids - used_indices = set() # Keep track of used indices if no replacement is allowed - - for i in range(len(test_scores)): # Iterate through each test score - match_id = i - score = test_scores.iloc[i] - if method == 'random': # If the method is random - bool_match = abs(ctrl_scores - score) <= threshold # Find all control scores within the threshold - matches = ctrl_scores.loc[bool_match[bool_match.scores].index] - elif method == 'min': # If the method is minimum difference - matches = abs(ctrl_scores - score).sort_values('scores').head(nmatches) # Find the closest scores + test_scores = self.data[self.data[self.yvar] == True][['scores']].sort_values('scores').reset_index() + ctrl_scores = self.data[self.data[self.yvar] == False][['scores']].sort_values('scores').reset_index() + + test_indices = test_scores['index'].values + test_scores_values = test_scores['scores'].values.reshape(-1, 1) + ctrl_indices = ctrl_scores['index'].values + ctrl_scores_values = ctrl_scores['scores'].values.reshape(-1, 1) + + # Initialize NearestNeighbors + nbrs = NearestNeighbors(n_neighbors=nmatches, radius=threshold, algorithm='ball_tree').fit(ctrl_scores_values) + + # Find neighbors within the threshold + distances, indices = nbrs.radius_neighbors(test_scores_values) + + matched_records = [] + current_match_id = 0 + used_ctrl_indices = set() if not replacement else None + + for i, neighbors in enumerate(indices): + if len(neighbors) == 0: + continue # No match found within threshold + if method == 'min': + # Sort neighbors by distance + sorted_neighbors = neighbors[np.argsort(distances[i])] + selected = [] + for neighbor in sorted_neighbors: + ctrl_idx = ctrl_indices[neighbor] + if not replacement and ctrl_idx in used_ctrl_indices: + continue + selected.append(ctrl_idx) + if not replacement: + used_ctrl_indices.add(ctrl_idx) + if len(selected) == nmatches: + break + if selected: + for ctrl_idx in selected: + matched_records.append({ + 'test_index': test_indices[i], + 'control_index': ctrl_idx, + 'match_id': current_match_id + }) + current_match_id += 1 + elif method == 'random': + # Randomly select up to nmatches from the neighbors + possible = list(neighbors) + if not replacement: + possible = [n for n in possible if ctrl_indices[n] not in used_ctrl_indices] + if len(possible) == 0: + continue + select = min(nmatches, len(possible)) + selected = np.random.choice(possible, size=select, replace=False).tolist() + for neighbor in selected: + ctrl_idx = ctrl_indices[neighbor] + matched_records.append({ + 'test_index': test_indices[i], + 'control_index': ctrl_idx, + 'match_id': current_match_id + }) + if not replacement: + used_ctrl_indices.add(ctrl_idx) + current_match_id += 1 else: raise ValueError("Invalid method parameter, use ('random', 'min')") - if len(matches) == 0: # If no matches are found, continue to the next - continue + # Convert matched_records to DataFrame + if matched_records: + matched_df = pd.DataFrame(matched_records) + matched_test = self.data.loc[matched_df['test_index']].copy() + matched_ctrl = self.data.loc[matched_df['control_index']].copy() - if not replacement: # If replacement is not allowed - matches = matches[~matches.index.isin(used_indices)] # Exclude already used indices + # Assign match_id and record_id + matched_test['match_id'] = matched_df['match_id'].values + matched_ctrl['match_id'] = matched_df['match_id'].values + matched_test['record_id'] = matched_test.index + matched_ctrl['record_id'] = matched_ctrl.index - if len(matches) == 0: # Check again if there are matches after filtering - continue - - select = nmatches if method != 'random' else np.random.choice(range(1, max_rand + 1), - 1) # Select number of matches - chosen = np.random.choice(matches.index, min(select, nmatches), - replace=False) # Choose the indices for matching - - if not replacement: # If no replacement, update the used indices - used_indices.update(chosen) - - result.extend([test_scores.index[i]] + list(chosen)) # Append the matched indices to the result - match_ids.extend([i] * (len(chosen) + 1)) # Append the match_id for each pair - - self.matched_data = self.data.loc[result] # Create the matched dataset - self.matched_data['match_id'] = match_ids # Assign match_id to each row in the matched dataset - self.matched_data['record_id'] = self.matched_data.index # Assign record_id to each row in the matched dataset + # Combine matched data + self.matched_data = pd.concat([matched_test, matched_ctrl], ignore_index=True) + else: + self.matched_data = pd.DataFrame(columns=self.data.columns.tolist() + ['match_id', 'record_id']) def plot_scores(self): """ @@ -279,16 +331,16 @@ def plot_scores(self): """ assert 'scores' in self.data.columns, \ "Propensity scores haven't been calculated, use Matcher.predict_scores()" - sns.distplot(self.data[self.data[self.yvar] == 0].scores, label='Control') - sns.distplot(self.data[self.data[self.yvar] == 1].scores, label='Test') + sns.kdeplot(self.data[self.data[self.yvar] == 0].scores, label='Control', fill=True) + sns.kdeplot(self.data[self.data[self.yvar] == 1].scores, label='Test', fill=True) plt.legend(loc='upper right') plt.xlim((0, 1)) plt.title("Propensity Scores Before Matching") - plt.ylabel("Percentage (%)") + plt.ylabel("Density") plt.xlabel("Scores") plt.show() - def prop_test(self, col): + def prop_test(self, col: str) -> Optional[dict]: """ Performs a Chi-Square test of independence on See stats.chi2_contingency() @@ -304,8 +356,6 @@ def prop_test(self, col): {'var': , 'before': , 'after': } - - """ if not uf.is_continuous(col, self.X) and col not in self.exclude: pval_before = round(stats.chi2_contingency(self.prep_prop_test(self.data, @@ -315,8 +365,9 @@ def prop_test(self, col): return {'var': col, 'before': pval_before, 'after': pval_after} else: logging.info(f"{col} is a continuous variable") + return None - def compare_continuous(self, save=False, return_table=False, plot_result=True): + def compare_continuous(self, save: bool = False, return_table: bool = False, plot_result: bool = True) -> Optional[pd.DataFrame]: """ Plots the ECDFs for continuous features before and after matching. Each chart title contains test results @@ -327,7 +378,7 @@ def compare_continuous(self, save=False, return_table=False, plot_result=True): Kolmogorov-Smirnov Goodness of fit Test (KS-test) This test statistic is calculated on 1000 permuted samples of the data, generating - an imperical p-value. See pysmatch.functions.ks_boot() + an empirical p-value. See pysmatch.functions.ks_boot() This is an adaptation of the ks.boot() method in the R "Matching" package https://www.rdocumentation.org/packages/Matching/versions/4.9-2/topics/ks.boot @@ -337,7 +388,7 @@ def compare_continuous(self, save=False, return_table=False, plot_result=True): See pysmatch.functions.grouped_permutation_test() Other included Stats: - Standarized mean and median differences + Standardized mean and median differences How many standard deviations away are the mean/median between our groups before and after matching i.e. abs(mean(control) - mean(test)) / std(control.union(test)) @@ -351,8 +402,6 @@ def compare_continuous(self, save=False, return_table=False, plot_result=True): ------- pd.DataFrame (optional) Table of before/after statistics if return_table == True - - """ test_results = [] for col in self.matched_data.columns: @@ -376,22 +425,22 @@ def compare_continuous(self, save=False, return_table=False, plot_result=True): f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(12, 5)) ax1.plot(xcb.x, xcb.y, label='Control', color=self.control_color) ax1.plot(xtb.x, xtb.y, label='Test', color=self.test_color) - ax1.plot(xcb.x, xcb.y, label='Control', color=self.control_color) - ax1.plot(xtb.x, xtb.y, label='Test', color=self.test_color) - - title_str = ''' - ECDF for {} {} Matching - KS p-value: {} - Grouped Perm p-value: {} - Std. Median Difference: {} - Std. Mean Difference: {} - ''' - ax1.set_title(title_str.format(col, "before", ksb, pb, - std_diff_med_before, std_diff_mean_before)) - ax2.plot(xca.x, xca.y, label='Control') - ax2.plot(xta.x, xta.y, label='Test') - ax2.set_title(title_str.format(col, "after", ksa, pa, - std_diff_med_after, std_diff_mean_after)) + ax1.set_title(f''' + ECDF for {col} before Matching + KS p-value: {ksb} + Grouped Perm p-value: {pb} + Std. Median Difference: {std_diff_med_before} + Std. Mean Difference: {std_diff_mean_before} + ''') + ax2.plot(xca.x, xca.y, label='Control', color=self.control_color) + ax2.plot(xta.x, xta.y, label='Test', color=self.test_color) + ax2.set_title(f''' + ECDF for {col} after Matching + KS p-value: {ksa} + Grouped Perm p-value: {pa} + Std. Median Difference: {std_diff_med_after} + Std. Mean Difference: {std_diff_mean_after} + ''') ax2.legend(loc="lower right") plt.xlim((0, np.percentile(xta.x, 99))) plt.show() @@ -422,10 +471,10 @@ def compare_continuous(self, save=False, return_table=False, plot_result=True): return pd.DataFrame(test_results)[var_order] if return_table else None - def compare_categorical(self, return_table=False, plot_result=True): + def compare_categorical(self, return_table: bool = False, plot_result: bool = True) -> Optional[pd.DataFrame]: """ Plots the proportional differences of each enumerated - discete column for test and control. + discrete column for test and control. i.e. - Each chart title contains the results from a Chi-Square Test of Independence before and after @@ -443,10 +492,9 @@ def compare_categorical(self, return_table=False, plot_result=True): pd.DataFrame() (optional) Table with the p-values of the Chi-Square contingency test for each discrete column before and after matching - """ - def prep_plot(data, var, colname): + def prep_plot(data: pd.DataFrame, var: str, colname: str) -> pd.DataFrame: t, c = data[data[self.yvar] == 1], data[data[self.yvar] == 0] # dummy var for counting dummy = [i for i in t.columns if i not in \ @@ -469,17 +517,19 @@ def prep_plot(data, var, colname): dafter = prep_plot(self.matched_data, col, colname="after") df = dbefore.join(dafter) test_results_i = self.prop_test(col) - test_results.append(test_results_i) - if plot_result: + if test_results_i is not None: + test_results.append(test_results_i) + if plot_result and test_results_i is not None: # plotting df.plot.bar(alpha=.8) plt.title(title_str.format(col, test_results_i["before"], test_results_i["after"])) lim = max(.09, abs(df).max().max()) + .01 plt.ylim((-lim, lim)) + plt.show() return pd.DataFrame(test_results)[['var', 'before', 'after']] if return_table else None - def prep_prop_test(self, data, var): + def prep_prop_test(self, data: pd.DataFrame, var: str) -> List[List[int]]: """ Helper method for running chi-square contingency tests @@ -502,38 +552,31 @@ def prep_prop_test(self, data, var): A table (list of lists) of counts for all enumerated field within for test and control groups. """ - counts = data.groupby([var, self.yvar]).count().reset_index() - table = [] - for t in (0, 1): - os_counts = counts[counts[self.yvar] == t] \ - .sort_values(var) - cdict = {} - for row in os_counts.iterrows(): - row = row[1] - cdict[row[var]] = row[2] - table.append(cdict) - # fill empty keys as 0 - all_keys = set(chain.from_iterable(table)) - for d in table: - d.update((k, 0) for k in all_keys if k not in d) - ctable = [[i[k] for k in sorted(all_keys)] for i in table] + counts = data.groupby([var, self.yvar]).size().unstack(fill_value=0) + # Ensure both classes 0 and 1 are present + if 0 not in counts.columns: + counts[0] = 0 + if 1 not in counts.columns: + counts[1] = 0 + counts = counts[[0, 1]] + ctable = counts.values.tolist() return ctable - def prop_retained(self): + def prop_retained(self) -> float: """ Returns the proportion of data retained after matching """ return len(self.matched_data[self.matched_data[self.yvar] == self.minority]) * 1.0 / \ len(self.data[self.data[self.yvar] == self.minority]) - def tune_threshold(self, method, nmatches=1, rng=np.arange(0, .001, .0001)): + def tune_threshold(self, method: str, nmatches: int = 1, rng: np.ndarray = np.arange(0, .001, .0001)): """ Matches data over a grid to optimize threshold value and plots results. Parameters ---------- method : str - Method used for matching (use "random" for this method) + Method used for matching (use "random" or "min" for this method) nmatches : int Max number of matches per record. See pysmatch.match() rng: : list / np.array() @@ -542,7 +585,6 @@ def tune_threshold(self, method, nmatches=1, rng=np.arange(0, .001, .0001)): Returns ------- None - """ results = [] for i in rng: @@ -552,12 +594,12 @@ def tune_threshold(self, method, nmatches=1, rng=np.arange(0, .001, .0001)): plt.title("Proportion of Data retained for grid of threshold values") plt.ylabel("Proportion Retained") plt.xlabel("Threshold") - plt.xticks(rng) + plt.xticks(rng, rotation=90) plt.show() - def record_frequency(self): + def record_frequency(self) -> pd.DataFrame: """ - Calculates the frequency of specifi records in + Calculates the frequency of specific records in the matched dataset Returns @@ -566,10 +608,8 @@ def record_frequency(self): Frequency table of the number records matched once, twice, ..., etc. """ - freqs = self.matched_data.groupby("record_id") \ - .count().groupby("match_id").count() \ - [["scores"]].reset_index() - freqs.columns = ["freq", "n_records"] + freqs = self.matched_data['match_id'].value_counts().reset_index() + freqs.columns = ['freq', 'n_records'] return freqs def assign_weight_vector(self): @@ -587,4 +627,4 @@ def _scores_to_accuracy(self, m, X, y): preds = (preds >= 0.5).astype(float) accuracy = np.mean(preds == y.to_numpy()) - return accuracy + return accuracy \ No newline at end of file diff --git a/pysmatch/__init__.py b/pysmatch/__init__.py index 386fa50..228c57a 100644 --- a/pysmatch/__init__.py +++ b/pysmatch/__init__.py @@ -4,7 +4,7 @@ from collections import Counter from itertools import chain import sys; sys.path.append(sys.argv[0]) -import pysmatch.functions as uf +import pysmatch.utils as uf import statsmodels.api as sm import patsy import seaborn as sns diff --git a/pysmatch/functions.py b/pysmatch/utils.py similarity index 100% rename from pysmatch/functions.py rename to pysmatch/utils.py