# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import numpy as np import pandas as pd import powerlaw import streamlit as st from scipy.stats import ks_2samp from scipy.stats import zipf as zipf_lib from .dataset_utils import CNT, PROP # treating inf values as NaN as well pd.set_option("use_inf_as_na", True) logs = logging.getLogger(__name__) logs.setLevel(logging.INFO) logs.propagate = False if not logs.handlers: # Logging info to log file file = logging.FileHandler("./log_files/zipf.log") fileformat = logging.Formatter("%(asctime)s:%(message)s") file.setLevel(logging.INFO) file.setFormatter(fileformat) # Logging debug messages to stream stream = logging.StreamHandler() streamformat = logging.Formatter("[data_measurements_tool] %(message)s") stream.setLevel(logging.WARNING) stream.setFormatter(streamformat) logs.addHandler(file) logs.addHandler(stream) class Zipf: def __init__(self, vocab_counts_df=pd.DataFrame()): self.vocab_counts_df = vocab_counts_df self.alpha = None self.xmin = None self.xmax = None self.fit = None self.ranked_words = {} self.uniq_counts = [] self.uniq_ranks = [] self.uniq_fit_counts = None self.term_df = None self.pvalue = None self.ks_test = None self.distance = None self.fit = None self.predicted_zipf_counts = None if not self.vocab_counts_df.empty: logs.info("Fitting based on input vocab counts.") self.calc_fit(vocab_counts_df) logs.info("Getting predicted counts.") self.predicted_zipf_counts = self.calc_zipf_counts(vocab_counts_df) def load(self, zipf_dict): self.set_xmin(zipf_dict["xmin"]) self.set_xmax(zipf_dict["xmax"]) self.set_alpha(zipf_dict["alpha"]) self.set_ks_distance(zipf_dict["ks_distance"]) self.set_p(zipf_dict["p-value"]) self.set_unique_ranks(zipf_dict["uniq_ranks"]) self.set_unique_counts(zipf_dict["uniq_counts"]) def calc_fit(self, vocab_counts_df): """ Uses the powerlaw package to fit the observed frequencies to a zipfian distribution. We use the KS-distance to fit, as that seems more appropriate that MLE. :param vocab_counts_df: :return: """ self.vocab_counts_df = vocab_counts_df # TODO: These proportions may have already been calculated. vocab_counts_df[PROP] = vocab_counts_df[CNT] / float(sum(vocab_counts_df[CNT])) rank_column = vocab_counts_df[CNT].rank( method="dense", numeric_only=True, ascending=False ) vocab_counts_df["rank"] = rank_column.astype("int64") observed_counts = vocab_counts_df[CNT].values # Note another method for determining alpha might be defined by # (Newman, 2005): alpha = 1 + n * sum(ln( xi / xmin )) ^ -1 self.fit = powerlaw.Fit(observed_counts, fit_method="KS", discrete=True) # This should probably be a pmf (not pdf); using discrete=True above. # original_data=False uses only the fitted data (within xmin and xmax). # pdf_bin_edges: The portion of the data within the bin. # observed_pdf: The probability density function (normalized histogram) # of the data. pdf_bin_edges, observed_pdf = self.fit.pdf(original_data=False) # See the 'Distribution' class described here for info: # https://pythonhosted.org/powerlaw/#powerlaw.Fit.pdf theoretical_distro = self.fit.power_law # The probability density function (normalized histogram) of the # theoretical distribution. predicted_pdf = theoretical_distro.pdf() # !!!! CRITICAL VALUE FOR ZIPF !!!! self.alpha = theoretical_distro.alpha # Exclusive xmin: The optimal xmin *beyond which* the scaling regime of # the power law fits best. self.xmin = theoretical_distro.xmin self.xmax = theoretical_distro.xmax self.distance = theoretical_distro.KS() self.ks_test = ks_2samp(observed_pdf, predicted_pdf) self.pvalue = self.ks_test[1] logs.info("KS test:") logs.info(self.ks_test) def set_xmax(self, xmax): """ xmax is usually None, so we add some handling to set it as the maximum rank in the dataset. :param xmax: :return: """ if xmax: self.xmax = int(xmax) elif self.uniq_counts: self.xmax = int(len(self.uniq_counts)) elif self.uniq_ranks: self.xmax = int(len(self.uniq_ranks)) def get_xmax(self): """ :return: """ if not self.xmax: self.set_xmax(self.xmax) return self.xmax def set_p(self, p): self.p = int(p) def get_p(self): return int(self.p) def set_xmin(self, xmin): self.xmin = xmin def get_xmin(self): if self.xmin: return int(self.xmin) return self.xmin def set_alpha(self, alpha): self.alpha = float(alpha) def get_alpha(self): return float(self.alpha) def set_ks_distance(self, distance): self.distance = float(distance) def get_ks_distance(self): return self.distance def calc_zipf_counts(self, vocab_counts_df): """ The fit is based on an optimal xmin (minimum rank) Let's use this to make count estimates for the zipf fit, by multiplying the fitted pmf value by the sum of counts above xmin. :return: array of count values following the fitted pmf. """ # TODO: Limit from above xmin to below xmax, not just above xmin. counts = vocab_counts_df[CNT] self.uniq_counts = list(pd.unique(counts)) self.uniq_ranks = list(np.arange(1, len(self.uniq_counts) + 1)) logs.info(self.uniq_counts) logs.info(self.xmin) logs.info(self.xmax) # Makes sure they are ints if not None xmin = self.get_xmin() xmax = self.get_xmax() self.uniq_fit_counts = self.uniq_counts[xmin + 1 : xmax] pmf_mass = float(sum(self.uniq_fit_counts)) zipf_counts = np.array( [self.estimate_count(rank, pmf_mass) for rank in self.uniq_ranks] ) return zipf_counts def estimate_count(self, rank, pmf_mass): return int(round(zipf_lib.pmf(rank, self.alpha) * pmf_mass)) def set_unique_ranks(self, ranks): self.uniq_ranks = ranks def get_unique_ranks(self): return self.uniq_ranks def get_unique_fit_counts(self): return self.uniq_fit_counts def set_unique_counts(self, counts): self.uniq_counts = counts def get_unique_counts(self): return self.uniq_counts def set_axes(self, unique_counts, unique_ranks): self.uniq_counts = unique_counts self.uniq_ranks = unique_ranks # TODO: Incorporate this function (not currently using) def fit_others(self, fit): st.markdown( "_Checking log likelihood ratio to see if the data is better explained by other well-behaved distributions..._" ) # The first value returned from distribution_compare is the log likelihood ratio better_distro = False trunc = fit.distribution_compare("power_law", "truncated_power_law") if trunc[0] < 0: st.markdown("Seems a truncated power law is a better fit.") better_distro = True lognormal = fit.distribution_compare("power_law", "lognormal") if lognormal[0] < 0: st.markdown("Seems a lognormal distribution is a better fit.") st.markdown("But don't panic -- that happens sometimes with language.") better_distro = True exponential = fit.distribution_compare("power_law", "exponential") if exponential[0] < 0: st.markdown("Seems an exponential distribution is a better fit. Panic.") better_distro = True if not better_distro: st.markdown("\nSeems your data is best fit by a power law. Celebrate!!")