diff --git a/_delphi_utils_python/delphi_utils/weekday.py b/_delphi_utils_python/delphi_utils/weekday.py index ba5d75815..735b2fdfd 100644 --- a/_delphi_utils_python/delphi_utils/weekday.py +++ b/_delphi_utils_python/delphi_utils/weekday.py @@ -12,14 +12,15 @@ class Weekday: """Class to handle weekday effects.""" @staticmethod - def get_params(data, denominator_col, numerator_cols, date_col, scales, logger): - r"""Fit weekday correction for each col in numerator_cols. + def get_params(data, denominator_col, numerator_cols, date_col, scales, logger, lm=10): + """Fit weekday correction for each col in numerator_cols. Return a matrix of parameters: the entire vector of betas, for each time series column in the data. + + If you want to weekday correction on counts (and not ratios), set denominator_col to None """ tmp = data.reset_index() - denoms = tmp.groupby(date_col).sum()[denominator_col] nums = tmp.groupby(date_col).sum()[numerator_cols] # Construct design matrix to have weekday indicator columns and then day @@ -30,21 +31,27 @@ def get_params(data, denominator_col, numerator_cols, date_col, scales, logger): X[np.where(nums.index.dayofweek == 6)[0], :6] = -1 X[:, 6:] = np.eye(X.shape[0]) - npnums, npdenoms = np.array(nums), np.array(denoms) + npnums = np.array(nums) params = np.zeros((nums.shape[1], X.shape[1])) + npdenoms = None + if denominator_col is not None: + denoms = tmp.groupby(date_col).sum()[denominator_col] + npdenoms = np.array(denoms) + # Loop over the available numerator columns and smooth each separately. for i in range(nums.shape[1]): - result = Weekday._fit(X, scales, npnums[:, i], npdenoms) + result = Weekday._fit(X, scales, npnums[:, i], npdenoms, lm) if result is None: logger.error("Unable to calculate weekday correction") + params[i, :] = result else: params[i,:] = result return params @staticmethod - def _fit(X, scales, npnums, npdenoms): + def _fit(X, scales, npnums, npdenoms, lm): r"""Correct a signal estimated as numerator/denominator for weekday effects. The ordinary estimate would be numerator_t/denominator_t for each time point @@ -78,16 +85,25 @@ def _fit(X, scales, npnums, npdenoms): ll = (numerator * (X*b + log(denominator)) - sum(exp(X*b) + log(denominator))) / num_days + + If you want to weekday correction on counts (and not ratios), remove the denom terms + from ll and set npdenoms to None. """ b = cp.Variable((X.shape[1])) lmbda = cp.Parameter(nonneg=True) - lmbda.value = 10 # Hard-coded for now, seems robust to changes + lmbda.value = lm # Hard-coded for now, seems robust to changes ll = ( - cp.matmul(npnums, cp.matmul(X, b) + np.log(npdenoms)) - - cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms))) - ) / X.shape[0] + cp.matmul(npnums, cp.matmul(X, b)) - + cp.sum(cp.exp(cp.matmul(X, b))) + ) / X.shape[0] + + if npdenoms is not None: + ll = ( + cp.matmul(npnums, cp.matmul(X, b) + np.log(npdenoms)) - + cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms))) + ) / X.shape[0] # L-1 Norm of third differences, rewards smoothness penalty = lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2) for scale in scales: diff --git a/chng_flags/.pylintrc b/chng_flags/.pylintrc new file mode 100644 index 000000000..f30837c7e --- /dev/null +++ b/chng_flags/.pylintrc @@ -0,0 +1,22 @@ + +[MESSAGES CONTROL] + +disable=logging-format-interpolation, + too-many-locals, + too-many-arguments, + # Allow pytest functions to be part of a class. + no-self-use, + # Allow pytest classes to have one test. + too-few-public-methods + +[BASIC] + +# Allow arbitrarily short-named variables. +variable-rgx=[a-z_][a-z0-9_]* +argument-rgx=[a-z_][a-z0-9_]* +attr-rgx=[a-z_][a-z0-9_]* + +[DESIGN] + +# Don't complain about pytest "unused" arguments. +ignored-argument-names=(_.*|run_as_module) \ No newline at end of file diff --git a/chng_flags/Makefile b/chng_flags/Makefile new file mode 100644 index 000000000..bc88f1fec --- /dev/null +++ b/chng_flags/Makefile @@ -0,0 +1,29 @@ +.PHONY = venv, lint, test, clean + +dir = $(shell find ./delphi_* -name __init__.py | grep -o 'delphi_[_[:alnum:]]*' | head -1) +venv: + python3.8 -m venv env + +install: venv + . env/bin/activate; \ + pip install wheel ; \ + pip install -e ../_delphi_utils_python ;\ + pip install -e . + +install-ci: venv + . env/bin/activate; \ + pip install wheel ; \ + pip install ../_delphi_utils_python ;\ + pip install . + +lint: + . env/bin/activate; pylint $(dir) + . env/bin/activate; pydocstyle $(dir) + +test: + . env/bin/activate ;\ + (cd tests && ../env/bin/pytest --cov=$(dir) --cov-report=term-missing) + +clean: + rm -rf env + rm -f params.json diff --git a/chng_flags/README.md b/chng_flags/README.md new file mode 100644 index 000000000..420931db7 --- /dev/null +++ b/chng_flags/README.md @@ -0,0 +1,62 @@ +# Change Flagging System + +## Running the System + +The system is run by directly executing the Python module contained in this +directory. The safest way to do this is to create a virtual environment, +installed the common DELPHI tools, and then install the module and its +dependencies. To do this, run the following code from this directory: + +``` +make install +``` + +This command will install the package in editable mode, so you can make changes that +will automatically propagate to the installed package. + +All of the user-changable parameters are stored in `params.json`. A template is +included as `params.json.template`. + +Uniquely to this project, ensure all your parameters are sensibe and input/output +folders exist with the desired files in them. + +``` +env/bin/python -m delphi_chng_flags +``` + +If you want to enter the virtual environment in your shell, +you can run `source env/bin/activate`. Run `deactivate` to leave the virtual environment. + +Once you are finished, you can remove the virtual environment and +params file with the following: + +``` +make clean +``` + +## Testing the code + +To run static tests of the code style, run the following command: + +``` +make lint +``` + +Unit tests are also included in the module. To execute these, run the following +command from this directory: + +``` +make test +``` + +To run individual tests, run the following: + +``` +(cd tests && ../env/bin/pytest .py --cov=delphi_chng_flags --cov-report=term-missing) +``` + +The output will show the number of unit tests that passed and failed, along +with the percentage of code covered by the tests. + +None of the linting or unit tests should fail, and the code lines that are not covered by unit tests should be small and +should not include critical sub-routines. diff --git a/chng_flags/REVIEW.md b/chng_flags/REVIEW.md new file mode 100644 index 000000000..0749edc96 --- /dev/null +++ b/chng_flags/REVIEW.md @@ -0,0 +1,39 @@ +## Code Review (Python) + +A code review of this module should include a careful look at the code and the +output. To assist in the process, but certainly not in replace of it, please +check the following items. + +**Documentation** + +- [x] the README.md file template is filled out and currently accurate; it is +possible to load and test the code using only the instructions given +- [x] minimal docstrings (one line describing what the function does) are +included for all functions; full docstrings describing the inputs and expected +outputs should be given for non-trivial functions + +**Structure** + +- [x] code should use 4 spaces for indentation; other style decisions are +flexible, but be consistent within a module +- [TODO] any required metadata files are checked into the repository and placed +within the directory `static` +- [TODO] any intermediate files that are created and stored by the module should +be placed in the directory `cache` +- [x] final expected output files to be uploaded to the API are placed in the +`receiving` directory; output files should not be committed to the respository +- [x] all options and API keys are passed through the file `params.json` +- [x] template parameter file (`params.json.template`) is checked into the +code; no personal (i.e., usernames) or private (i.e., API keys) information is +included in this template file + +**Testing** + +- [x] module can be installed in a new virtual environment +- [x] pylint with the default `.pylint` settings run over the module produces +minimal warnings; warnings that do exist have been confirmed as false positives +- [TODO] reasonably high level of unit test coverage covering all of the main logic +of the code (e.g., missing coverage for raised errors that do not currently seem +possible to reach are okay; missing coverage for options that will be needed are +not) +- [TODO] all unit tests run without errors diff --git a/chng_flags/cache/.gitignore b/chng_flags/cache/.gitignore new file mode 100644 index 000000000..afed0735d --- /dev/null +++ b/chng_flags/cache/.gitignore @@ -0,0 +1 @@ +*.csv diff --git a/chng_flags/delphi_chng_flags/__init__.py b/chng_flags/delphi_chng_flags/__init__.py new file mode 100644 index 000000000..1ec70a1e1 --- /dev/null +++ b/chng_flags/delphi_chng_flags/__init__.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +"""Module to pull and clean from CHC source flagging. + +This file defines the functions that are made public by the module. As the +module is intended to be executed though the main method, these are primarily +for testing. +""" + +from __future__ import absolute_import + +from . import pull +from . import run + +__version__ = "0.0.0" diff --git a/chng_flags/delphi_chng_flags/__main__.py b/chng_flags/delphi_chng_flags/__main__.py new file mode 100644 index 000000000..32fc0eecc --- /dev/null +++ b/chng_flags/delphi_chng_flags/__main__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +"""Call the function run_module when executed. + +This file indicates that calling the module (`python -m MODULE_NAME`) will +call the function `run_module` found within the run.py file. There should be +no need to change this template. +""" + +from delphi_utils import read_params +from .run import run_module # pragma: no cover + +run_module(read_params()) # pragma: no cover diff --git a/chng_flags/delphi_chng_flags/data_transform.py b/chng_flags/delphi_chng_flags/data_transform.py new file mode 100644 index 000000000..5a3565e9f --- /dev/null +++ b/chng_flags/delphi_chng_flags/data_transform.py @@ -0,0 +1,177 @@ +"""Functions to raise flags, transform, and correct data.""" +import numpy as np +import pandas as pd +import scipy.stats as stats +import statsmodels.formula.api as smf + + +def identify_correct_spikes(df: pd.DataFrame) -> (pd.DataFrame, pd.DataFrame): + """We want to find large spikes in data and then correct them. + + We do this by considering the difference between weekdays ex: Sun-Mon + and identifying points that have a z-score > 3 for each of the weekday diffs (6 categories). + + Then, we correct those points by using the median of + the weekday diff for that particular category in sequential order. + + Input: A raw dataframe + + Return: A dataframe with spikes corrected, flagged points list + """ + #TO DO: Add in the header of this dataframe + diff_df = df.drop(columns=['end', 'day']).diff(1).dropna() + diff_df['day'] = df['day'] + medians = [] + for _, (_, ldf) in enumerate(diff_df.groupby(['day'])): + medians.append(ldf.apply(lambda x: np.median(pd.Series(x).values), axis=0)) + diff_df = diff_df.drop(columns=['day']) + points_out = diff_df.apply(lambda x: list(x[stats.zscore(x) > 3].index) + + list(x[stats.zscore(x) < -3].index), axis=0) + medians_df = pd.concat(medians, axis=1).T + point_out_df = [] + for state, points_list in zip(list(points_out.index), points_out): + point_out_df.append(df.loc[points_list, state]) + + for po in points_list: + day_week = df.loc[po, 'day'] + po2 = po - pd.Timedelta('1d') + df.loc[po, state] = df.loc[po2, state] + medians_df.loc[day_week, state] + return df, pd.DataFrame(point_out_df).stack() + + +def weekend_corr(df: pd.DataFrame, states: list) -> pd.DataFrame: + """Use to correct the weekend volume. + + We correct the volume so that we can use the + weekday effect correction which is a multiplicative method + and performs poorly when the values for weekday counts are low. + + Input: Dataframe with large spikes correction + List of states + + Returns: Dataframe with the weekends corrected + """ + + def create_wknum(df: pd.DataFrame) -> pd.DataFrame: + """Add a weeknumber to the dataframe. + + Input: A dataframe where the index is the dates. + + Returns: A dataframe with a weeknum column. + + We want each week to start with Saturday and Sunday. + """ + wk = 0 + wknum = [] + prev_val = 0 + for val in [int(str(x.isocalendar()[1])) for x in df.index]: + if val != prev_val: + wk += 1 + prev_val = val + wknum.append(wk) + df['weeknum'] = wknum + indices = df.query("day in [5,6]").index + df.loc[indices, 'weeknum'] = df.loc[indices, 'weeknum'] + 1 + return df + + df = create_wknum(df) + sat_p = [] + sun_p = [] + for _, df2 in df.groupby(['weeknum']): + if df2.shape[0] == 7: + total_counts = df2[states][~df2.day.isin([5, 6])].sum() + sat_p.append(df2[states][df2.day == 5].values[0] / total_counts) + sun_p.append(df2[states][df2.day == 6].values[0] / total_counts) + sat_vcorr = np.array([x if x > 0 else 0 for x in 0.2 - sum(sat_p) / len(sat_p)]) + sun_vcorr = np.array([x if x > 0 else 0 for x in 0.2 - sum(sun_p) / len(sun_p)]) + last_sat = 0 + last_sun = 0 + for _, df2 in df.groupby(['weeknum']): + if df2.shape[0] == 7: + total_counts = df2[states][~df2.day.isin([5, 6])].sum() + df.loc[df2.index[df2.day == 5], states] += sat_vcorr * total_counts + last_sat = sat_vcorr * total_counts + df.loc[df2.index[df2.day == 6], states] += sun_vcorr * total_counts + last_sun = sun_vcorr * total_counts + else: + df.loc[df2.index[df2.day == 5], states] = df.loc[df2.index[df2.day == 5], + states] + last_sat + df.loc[df2.index[df2.day == 6], states] = df.loc[df2.index[df2.day == 6], + states] + last_sun + return df.reset_index().rename(columns={'index': 'date'}) + + +def ar_method(df: pd.DataFrame, states: list, num_lags: int, n_train: int, + n_test: int, n_valid: int, replace_df: pd.DataFrame) \ + -> (pd.DataFrame, pd.DataFrame): + """Use an AR forecaster to predict the values of the current day. + + The parameters to the AR method is num_lags: the number of lags. + + For each of the n_test days, we create an AR model using n_train number + of data points per state. + Each model is tested for the n_test day and we save the residual. + We create a residual distribution from #states * n_train number of residuals, + which is used to rank the residuals from the days in n_valid. + Inputs: + df: Dataframe for the forecaster + states: The names of the states + num_lags: The number of lags for the AR model + n_train: Number of data points to train each AR model + n_test: Number of AR models per state to create residual distribution + n_valid: Using the residual distribution to rank the points in n_valid. + file_resid: Caching from any residual with compatible parameters + Output: + replace_df: A dataframe of residuals + resid_valid: A dataframe of points and their rank as flags + """ + dates_covered = [] + valid_day_list = list(df.date[-n_valid:]) + test_day_list = list(df.date[-n_test - n_valid:-n_valid]) + + if not replace_df.empty: + replace_df_orig = replace_df.reset_index().drop(columns=['lags','key']) + replace_df_orig['date'] = pd.to_datetime(replace_df_orig['date']) + dates_covered = np.unique(replace_df_orig.date) + curr_day_list = df.date[-n_test - n_valid:][~df.date.isin(dates_covered)] + replace_df = df.query('date in @curr_day_list')[states + + ['date']].set_index('date').stack().reset_index() + replace_df.columns = ['date', 'state', 'y'] + replace_df['y_pred'] = replace_df['y'] + + + lags_names = [f'lags_{i}' for i in range(1, num_lags + 1)] + model_str = 'model ~ ' + '+'.join(lags_names) + + for curr_day in curr_day_list: + start_train = curr_day - pd.Timedelta(days=n_train) + all_tmp = df[start_train <= df.date] + all_tmp = all_tmp[all_tmp.date <= curr_day][states] + + def pred_val(col): + state_df = pd.DataFrame() + state_df['model'] = col + for i in range(1, num_lags + 1): + state_df[f'lags_{i}'] = state_df['model'].shift(i) + state_df = state_df.dropna() + res_ols = smf.ols(model_str, state_df.iloc[:-2, :]).fit() + y_t_pred = np.array(res_ols.predict(state_df.iloc[-2, :])) + return y_t_pred[0] + + y_t_state = all_tmp.apply(pred_val, axis=0, result_type='reduce').T + replace_df.loc[(replace_df.date == curr_day), + 'y_pred'] = y_t_state.values + + replace_df['resid'] = round((replace_df['y_pred'] - + replace_df['y']) / replace_df['y'], 6) + + resid_all = replace_df[replace_df.date.isin(test_day_list)]['resid'].values + resid_valid = replace_df[replace_df.date.isin(valid_day_list)] + + resid_valid['cdf'] = resid_valid['resid'].apply(lambda x: + (len(resid_all[resid_all < x]) / len(resid_all))) + resid_valid['sort_prio'] = resid_valid['cdf'].apply(lambda x: x if x < 0.5 else 1 - x) + resid_valid = resid_valid.reset_index(drop=True) + resid_valid['state'] = resid_valid['state'].astype(int) + + return replace_df, resid_valid.drop_duplicates() diff --git a/chng_flags/delphi_chng_flags/pull.py b/chng_flags/delphi_chng_flags/pull.py new file mode 100644 index 000000000..dcdb4f525 --- /dev/null +++ b/chng_flags/delphi_chng_flags/pull.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*-\ +"""Pull relevant input data from the input cache (defined in params.json).""" +import glob +from datetime import date +import pandas as pd +import numpy as np + + +def pull_lags_data(cache_dir, lags, start_date, end_date, reset=False): + """Import num & den counts files and update as needed using data from the SFTP.""" + def make_df(file_names, lags): + #TO DO: Make clear that this is only for CHC data + """Create the lags from each input filename.""" + df_list = [] + for file_date in file_names.index: + df = file_names.loc[file_date, :] + new_df = pd.read_csv(df.fname, header=None, dtype={ + 0: 'str', + 1: 'str', + 2: 'str' + }, parse_dates=[0]) + new_df.columns = ['date', 'state', 'counts'] + new_df['date'] = pd.to_datetime(new_df['date'], format='%Y%m%d', errors='coerce') + const_day = 10 # a threshold we never want to go below for the adjusted lags + # threshold is at least lag size + dates_dict = {} + for x in lags: + tmp_date = file_date - pd.Timedelta(days=df.win_sub + + max(x-df.win_sub, min(const_day, x))) + dates_dict[tmp_date] = x + new_df = new_df[new_df.date.isin(dates_dict.keys())] + new_df['lags'] = new_df['date'].map(dates_dict) + new_df = new_df[pd.to_numeric(new_df['state'], errors='coerce').notnull()] + new_df['state'] = new_df.state.astype(int).astype(str).str.zfill(5).str[:2].astype(int) + new_df = new_df.query('state<57') + new_df['counts'] = new_df['counts'].replace("3 or less", 2).astype(int) + new_df = new_df.drop(columns=['date']).groupby(['lags', 'state']).\ + sum().sort_values(by=['lags', 'state']) + new_df.columns = [file_date] + df_list.append(new_df) + tmp = pd.concat(df_list, axis=1) + return tmp + + def file_update(cache_dir, str_file, lags, start_date, end_date, reset=False): + """Determine data needed and place preprocessed data nicely into an output dataframe.""" + if end_date is None: + end_date = date.today() + assert end_date.date() <= date.today() ,\ + "End date cannot exceed today's date" + dates_range = pd.date_range(start_date, end_date) + df = pd.DataFrame(columns=['lags', 'state']) + list_fname = glob.glob(f'{cache_dir}/{str_file}.csv') + if len(list_fname) == 1 and not reset: + df = pd.read_csv(list_fname[0], header=0).fillna(0).astype(int) + existing_lags = np.unique(df['lags']).astype(int) + df = df.set_index(['lags', 'state']) + df.columns = pd.to_datetime(df.columns) + existing_dates = list(filter(lambda x: x in dates_range, df.columns)) + missing_lags = list(filter(lambda x: x not in existing_lags, lags)) + missing_dates = list(filter(lambda x: pd.to_datetime(x, + format="%Y%m%d") not in existing_dates, dates_range)) + + rel_files = pd.DataFrame() + rel_files['fname'] = glob.glob(f'{cache_dir}/*{str_file}.dat.gz') + rel_files['fdate'] = pd.to_datetime( + rel_files['fname'].str.rsplit('/', n=1, expand=True)[1]. + str.split('_', n=1, expand=True)[0], + format='%Y%m%d', errors='coerce') + rel_files = rel_files.set_index('fdate') + merge_files = pd.DataFrame(index=dates_range) + rel_files = merge_files.merge(rel_files, how='outer', left_index=True, + right_index=True).fillna(method='ffill') + rel_files['win_sub'] = list(rel_files.reset_index(drop=True).groupby(['fname']).cumcount()) + if (len(missing_lags) > 0) and len(existing_dates) > 0: + sel_rel_files = rel_files.query('index in @existing_dates').sort_index() + df = pd.concat([df, make_df(sel_rel_files, missing_lags)]).fillna(0) + sel_rel_files = rel_files[rel_files.index.isin(missing_dates)].sort_index() + if sel_rel_files.shape[0] > 0: + df = pd.concat([df, make_df(sel_rel_files, lags)], axis=1).fillna(0) + df.to_csv(f'{cache_dir}/{str_file}.csv') + return df[dates_range] + + df_num = file_update(cache_dir, 'Covid', lags, start_date, end_date, reset) + df_den = file_update(cache_dir, 'Denom', lags, start_date, end_date, reset) + return df_num, df_den + + +def pull_csvs(cache_dir, num_lags, n_train): + """Pull additional files from input cache folder if they exist given params.""" + df_resid = pd.DataFrame(columns=['date', 'state', 'lags', 'key']) + flags_1_df = pd.DataFrame(columns=['date', 'state', 'lags', 'key']) + flags_2_df = pd.DataFrame(columns=['date', 'state', 'lags', 'key']) + files_list = glob.glob(f'{cache_dir}/resid_{n_train}_{num_lags}.csv') + if len(files_list) == 1: + df_resid = pd.read_csv(files_list[0]).drop_duplicates() + df_resid['date'] = pd.to_datetime(df_resid['date']) + files_list = glob.glob(f'{cache_dir}/flag_spike_{n_train}_{num_lags}.csv') + if len(files_list) == 1: + flags_1_df = pd.read_csv(files_list[0], dtype={'lags':int, 'key':str, + 'date':str, 'state':int, + 'val':float}).drop_duplicates() + flags_1_df['date'] = pd.to_datetime(flags_1_df['date']) + files_list = glob.glob(f'{cache_dir}/flag_ar_{n_train}_{num_lags}.csv') + if len(files_list) == 1: + flags_2_df = pd.read_csv(files_list[0]).drop_duplicates() + flags_2_df['date'] = pd.to_datetime(flags_2_df['date']) + df_resid = df_resid.set_index(['lags', 'key', 'date', 'state']) + flags_1_df = flags_1_df.set_index(['lags', 'key', 'date', 'state']) + flags_2_df = flags_2_df.set_index(['lags', 'key', 'date', 'state']) + return df_resid, flags_1_df, flags_2_df diff --git a/chng_flags/delphi_chng_flags/run.py b/chng_flags/delphi_chng_flags/run.py new file mode 100644 index 000000000..37e2b835c --- /dev/null +++ b/chng_flags/delphi_chng_flags/run.py @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- +"""Functions to call when running the function. + +This module should contain a function called `run_module`, that is executed +when the module is run with `python -m MODULE_NAME`. +""" +import time +import pandas as pd +from delphi_utils import ( + get_structured_logger, + Weekday +) +from .data_transform import ( + identify_correct_spikes, + weekend_corr, + ar_method) +from .pull import (pull_csvs, + pull_lags_data) + + +def run_module(params): + """Run Change Flag test module. + + The `params` argument is expected to have the following structure: + - "common": + - "export_dir": str, directory to write output + - "log_exceptions" (optional): bool, whether to log exceptions to file + - indicator": + - "input_cache_dir": str, directory in which to cache input data + - "num_lags": int, the number of lags for the AR model + - "n_train": int, the number of datapoints to train the AR model on + - "n_test": int, the number of days to create the residual distribution + - "n_valid": int, the number of days to rank + - "lags": list of ints, the windows (lags) to run the flagging program on. + A lag of 1 means the data that was received 1 day after the service. + """ + #TO DO Remove all prints + #TO DO Check all fn defns + start_time = time.time() + logger = get_structured_logger( + __name__, filename=params["common"].get("log_filename"), + log_exceptions=params["common"].get("log_exceptions", True)) + cache_dir = params["indicator"]["input_cache_dir"] + export_dir = params["common"]["export_dir"] + lags = params["indicator"]["lags"] + num_lags = params["indicator"]["num_lags"] + n_train = params["indicator"]["n_train"] + n_test = params["indicator"]["n_test"] + n_valid = params["indicator"]["n_valid"] + + start_date = pd.to_datetime(params["indicator"]["start_date"]) + end_date = pd.to_datetime(params["indicator"]["end_date"]) + assert num_lags < n_train, \ + "The number of lags you use for the AR model has to \ + be less than the number of samples you train on." + assert start_date <= end_date, \ + "Start date cannot exceed end_date" + # TO DO: change assert statements per signal! + # TO DO: Other validation statements. + # The valid + test + train has to be <= than the total date range + assert start_date > pd.to_datetime("03/01/2020"), \ + "Start date must be after March 1st, 2020" + + df_num, df_den = pull_lags_data(cache_dir, lags, start_date, end_date) + df_num.reset_index(inplace=True) + df_den.reset_index(inplace=True) + resid_df, flags_1_df, flags_2_df = pull_csvs(cache_dir, num_lags, n_train) + + export_files = {'resid_list': resid_df, + 'flags1_list': flags_1_df, + 'flags2_list': flags_2_df} + for lag in lags: + df_dict = {} + df_dict['w_num'] = df_num[df_num.lags == lag].\ + drop(columns=['lags']).set_index('state').T.fillna(0) + df_dict['w_den'] = df_den[df_den.lags == lag].\ + drop(columns=['lags']).set_index('state').T.fillna(0) + df_dict['ratio'] = df_dict['w_num'] / df_dict['w_den'].fillna(0) + for key, df in df_dict.items(): + df.columns = df.columns.astype(str) + df.index = pd.to_datetime(df.index) + df['day'] = [x.weekday() for x in list(df.index)] + df['end'] = [x.weekday() in [5, 6] for x in list(df.index)] + states = df.drop(columns=['end', 'day']).columns + spikes_df, flags = identify_correct_spikes(df.copy()) + flags = flags.reset_index() + flags.columns = ['state', 'date', 'val'] + weekend_df = weekend_corr(spikes_df.copy(), states) + params = Weekday.get_params(weekend_df.copy(), None, states, 'date', + [1, 1e5, 1e10, 1e15], logger, 10) + weekday_corr = Weekday.calc_adjustment(params + , weekend_df.copy(), + states, 'date').fillna(0) + + resid, flags2 = ar_method(weekday_corr.copy(), list(states), num_lags, n_train, + n_test, n_valid, resid_df) + + for tmp_df, ct in zip([resid, flags, flags2], + ['resid_list', 'flags1_list', 'flags2_list']): + tmp_df['lags'] = lag + tmp_df['key'] = key + tmp_df = tmp_df.set_index(['lags', 'key', 'date', 'state']) + tmp_append = pd.concat([export_files[ct], tmp_df]) + tmp_append = tmp_append.reset_index().drop_duplicates() + export_files[ct] = tmp_append.set_index(['lags', 'key', 'date', 'state']) + + export_files['flags2_list'] = export_files['flags2_list'].sort_values( + by=['sort_prio', 'lags', 'key', 'date']) + export_files['resid_list'].to_csv(f'{cache_dir}/resid_{n_train}_{num_lags}.csv') + export_files['flags1_list'].to_csv(f'{cache_dir}/flag_spike_{n_train}_{num_lags}.csv') + export_files['flags2_list'].to_csv(f'{cache_dir}/flag_ar_{n_train}_{num_lags}.csv') + + export_files['flags1_list'].to_csv(f'{export_dir}/flag_spike_{n_train}_{num_lags}.csv') + export_files['flags2_list'].to_csv(f'{export_dir}/flag_ar_{n_train}_{num_lags}.csv') + + elapsed_time_in_seconds = round(time.time() - start_time, 2) + logger.info("Completed indicator run", + elapsed_time_in_seconds=elapsed_time_in_seconds) diff --git a/chng_flags/instruct.sh b/chng_flags/instruct.sh new file mode 100644 index 000000000..a906281fe --- /dev/null +++ b/chng_flags/instruct.sh @@ -0,0 +1,3 @@ +#!/bin/zsh +make install + diff --git a/chng_flags/params.json.template b/chng_flags/params.json.template new file mode 100644 index 000000000..3e14aa462 --- /dev/null +++ b/chng_flags/params.json.template @@ -0,0 +1,14 @@ +{ + "common": { + "export_dir": "./receiving", + "log_exceptions": false + }, + "indicator": { + "input_cache_dir": "./cache", + "num_lags":7, + "n_train":300, + "n_test":100, + "n_valid":1, + "lags": [1, 3, 8, 20, 50, 60] + } +} diff --git a/chng_flags/receiving/.gitignore b/chng_flags/receiving/.gitignore new file mode 100644 index 000000000..afed0735d --- /dev/null +++ b/chng_flags/receiving/.gitignore @@ -0,0 +1 @@ +*.csv diff --git a/chng_flags/setup.py b/chng_flags/setup.py new file mode 100644 index 000000000..08dfb926a --- /dev/null +++ b/chng_flags/setup.py @@ -0,0 +1,33 @@ +from setuptools import setup +from setuptools import find_packages + +required = [ + "numpy", + "pandas", + "pydocstyle", + "pytest", + "pytest-cov", + "pylint==2.8.3", + "delphi-utils", + "imap-tools", + "xlrd==1.2.0", + "covidcast", + "openpyxl", + "statsmodels" +] + +setup( + name="delphi_changehc", + version="0.1.0", + description="Indicators Pulled from datadrop email account", + author="", + author_email="", + url="https://github.com/cmu-delphi/covidcast-indicators", + install_requires=required, + classifiers=[ + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3.8", + ], + packages=find_packages(), +) diff --git a/chng_flags/tests/cache/small_flags_ar.csv b/chng_flags/tests/cache/small_flags_ar.csv new file mode 100644 index 000000000..42969dd80 --- /dev/null +++ b/chng_flags/tests/cache/small_flags_ar.csv @@ -0,0 +1,52 @@ +,date,state,y,y_pred,resid,cdf,sort_prio +0,2022-01-24,1,0.0199,0.010000203568845826,-0.497477,0.0196078431372549,0.0196078431372549 +1,2022-01-24,2,0.0252,0.01530004985865285,-0.392855,0.0196078431372549,0.0196078431372549 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b/chng_flags/tests/cache/small_resid.csv new file mode 100644 index 000000000..ad01bb528 --- /dev/null +++ b/chng_flags/tests/cache/small_resid.csv @@ -0,0 +1,154 @@ +,date,state,y,y_pred,resid +0,2022-01-22,1,0.01,0.006999840358513567,-0.300016 +1,2022-01-22,2,0.0153,0.014800514307182693,-0.032646 +2,2022-01-22,4,0.0162,0.013699901945907715,-0.154327 +3,2022-01-22,5,0.0124,0.008999883116613178,-0.274203 +4,2022-01-22,6,0.0073,0.006699960809236129,-0.082197 +5,2022-01-22,8,0.0238,0.0214005225852852,-0.100818 +6,2022-01-22,9,0.0088,0.007500063444452418,-0.14772 +7,2022-01-22,10,0.0094,0.008199916126893813,-0.127668 +8,2022-01-22,11,0.0019,0.015398759903767703,7.10461 +9,2022-01-22,12,0.0052,0.003899987910479349,-0.250002 +10,2022-01-22,13,0.0111,0.008899844283697004,-0.198212 +11,2022-01-22,15,0.002,0.0009999984300102054,-0.500001 +12,2022-01-22,16,0.0117,0.008699809686388551,-0.256427 +13,2022-01-22,17,0.0096,0.00850003688477638,-0.114579 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Weekday +) + +def test_identify_correct_spikes(): + cache = "./cache/test_data_transforms" + df = pd.read_csv(f'{cache}/df.csv', index_col=0, parse_dates=[0]) + spikes_df, flags = identify_correct_spikes(df) + assert_frame_equal(spikes_df, pd.read_csv(f'{cache}/spikes_df.csv', index_col=0, parse_dates=[0])) + assert flags.empty + +def test_weekend_corr(): + cache = "./cache/test_data_transforms" + spikes_df = pd.read_csv(f'{cache}/spikes_df.csv', index_col=0, parse_dates=[0]) + wkc = weekend_corr(spikes_df, spikes_df.drop(columns=['end', 'day']).columns) + assert_frame_equal(wkc, pd.read_csv(f'{cache}/weekend_df.csv', parse_dates=[0])) + +def test_ar_method_known(): + cache = "./cache/test_data_transforms" + weekend_df = pd.read_csv(f'{cache}/weekend_df.csv', parse_dates=[0]) + states = weekend_df.drop(columns=['date', 'end', 'day', 'weeknum']).columns + params = Weekday.get_params(weekend_df.copy(), None, states, 'date', + [1, 1e5, 1e10, 1e15], None, 10) + weekday_corr = Weekday.calc_adjustment(params + , weekend_df.copy(), + states, 'date').fillna(0).round(4) + assert_frame_equal(weekday_corr, pd.read_csv(f'{cache}/weekday_corr.csv', parse_dates=[0]).round(4)) + + resid, flags2 = ar_method(weekday_corr, list(states), 2, 4, 2, 1, pd.DataFrame(columns=['date', 'state', 'lags', 'key'])) + assert_frame_equal(resid, pd.read_csv(f'./cache/small_resid.csv', index_col=0, parse_dates=[1], dtype={'date':str, 'state':object})) + assert_frame_equal(flags2, pd.read_csv(f'./cache/small_flags_ar.csv', index_col=0, parse_dates=[1])) + + rd = pd.read_csv(f'{cache}/resid_4_2.csv') + resid, flags2 = ar_method(weekday_corr, list(states), 2, 4, 2, 1, rd) + assert resid.empty + assert flags2.empty + diff --git a/chng_flags/tests/test_pull.py b/chng_flags/tests/test_pull.py new file mode 100644 index 000000000..ab23fb74c --- /dev/null +++ b/chng_flags/tests/test_pull.py @@ -0,0 +1,119 @@ +"""Tests for pulling files.""" +import numpy as np +import pandas as pd +from pandas.testing import assert_frame_equal +import os +from delphi_chng_flags.pull import ( + pull_lags_data, + pull_csvs) +import pytest +import shutil + + + +def test_exist_csvs(): + """See if the method returns the proper csv files given certain params""" + cache = 'cache/test_cache_with_file' + train_n = 4 + lags = 2 + df_resid, flags_1_df, flags_2_df = pull_csvs(cache,lags,train_n) + assert_frame_equal(df_resid.reset_index(), pd.read_csv(cache + f'/resid_{train_n}_{lags}.csv', header=0, parse_dates=['date']).drop_duplicates(), check_dtype=False) + assert_frame_equal(flags_1_df.reset_index(), pd.read_csv(cache + f'/flag_spike_{train_n}_{lags}.csv', header=0, parse_dates=['date']).drop_duplicates(), check_dtype=False) + assert_frame_equal(flags_2_df.reset_index(), pd.read_csv(cache + f'/flag_ar_{train_n}_{lags}.csv', header=0, parse_dates=['date']).drop_duplicates(), check_dtype=False) + +def test_missing_csvs(): + """See if the method returns the proper csv files when missing params""" + df_resid, flags_1_df, flags_2_df = pull_csvs('cache/test_cache_with_file', 6, 50) + ref = pd.DataFrame(columns=['date', 'state', 'lags', 'key']).set_index(['lags', 'key', 'date', 'state']) + assert df_resid.empty is True + assert flags_1_df.empty is True + assert flags_2_df.empty is True + assert df_resid.index.equals(ref.index) + assert flags_1_df.index.equals(ref.index) + assert flags_2_df.index.equals(ref.index) + + + +def test_lags_file(): + """Create a new Covid and Denom file from SFTP files using a set start and end date""" + cache_dir ="./cache/test_cache_create_files" + lags = [60] + start_date = "07/01/2021" + end_date = "07/02/2021" + df_num, df_den = pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date), reset=True) + df_num = df_num.reset_index() + df_den = df_den.reset_index() + comp_num = pd.read_csv(cache_dir + '/Covid_Intermediate.csv', header=0).reset_index(drop=True) + comp_den = pd.read_csv(cache_dir + '/Denom_Intermediate.csv', header=0).reset_index(drop=True) + comp_num.columns = list(comp_num.columns[:2]) + list([pd.to_datetime(x) for x in comp_num.columns[2:]]) + comp_den.columns = list(comp_den.columns[:2]) + list([pd.to_datetime(x) for x in comp_den.columns[2:]]) + assert_frame_equal(comp_num, df_num, check_dtype=False) + assert_frame_equal(comp_den, df_den, check_dtype=False) + # remove files + os.remove(cache_dir + '/Covid.csv') + os.remove(cache_dir + '/Denom.csv') + + +def test_add_lags_dates_missing(): + """Create a new Covid and Denom file from SFTP files where there are missing raw files. Also tests adding new lags and days to a dataframe.""" + cache_dir = "./cache/test_cache_create_files" + shutil.copy(cache_dir + '/Covid_Intermediate.csv', cache_dir + '/Covid.csv') + shutil.copy(cache_dir + '/Denom_Intermediate.csv', cache_dir + '/Denom.csv') + start_date = "07/01/2021" + end_date = "07/05/2021" + lags = [60, 1] + df_num, df_den = pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date)) + df_num = df_num.reset_index() + df_den = df_den.reset_index() + comp_num = pd.read_csv(cache_dir + '/test_Covid.csv', header=0).reset_index(drop=True).fillna(0).astype(int) + comp_den = pd.read_csv(cache_dir + '/test_Denom.csv', header=0).reset_index(drop=True).fillna(0).astype(int) + comp_num.columns = list(comp_num.columns[:2]) + list([pd.to_datetime(x) for x in comp_num.columns[2:]]) + comp_den.columns = list(comp_den.columns[:2]) + list([pd.to_datetime(x) for x in comp_den.columns[2:]]) + assert_frame_equal(comp_num, df_num, check_dtype=False) + assert_frame_equal(comp_den, df_den, check_dtype=False) + # remove files + os.remove(cache_dir + '/Covid.csv') + os.remove(cache_dir + '/Denom.csv') + +def test_start_end_date(): + """same start and end date""" + cache_dir = "./cache/test_cache_create_files" + shutil.copy(cache_dir + '/Covid_Intermediate.csv', cache_dir + '/Covid.csv') + shutil.copy(cache_dir + '/Denom_Intermediate.csv', cache_dir + '/Denom.csv') + start_date = "07/01/2021" + end_date = start_date + lags = [60] #can't use lag 1 bv of missing data issue + df_num, df_den = pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date)) + df_num = df_num.reset_index() + df_den = df_den.reset_index() + comp_num = pd.read_csv(cache_dir + '/test_Covid.csv', header=0).reset_index(drop=True).fillna(0).astype(int) + comp_den = pd.read_csv(cache_dir + '/test_Denom.csv', header=0).reset_index(drop=True).fillna(0).astype(int) + comp_num.columns = list(comp_num.columns[:2]) + list([pd.to_datetime(x) for x in comp_num.columns[2:]]) + comp_den.columns = list(comp_den.columns[:2]) + list([pd.to_datetime(x) for x in comp_den.columns[2:]]) + assert_frame_equal(comp_num.query('lags==60').iloc[:, :3], df_num, check_dtype=False) + assert_frame_equal(comp_den.query('lags==60').iloc[:, :3], df_den, check_dtype=False) + # remove files + os.remove(cache_dir + '/Covid.csv') + os.remove(cache_dir + '/Denom.csv') + + +def assert_fails(): + """Start date exceeds end date""" + end_date = "07/20/2021" + start_date = "08/02/2022" + with pytest.raises(AssertionError): + pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date)) + + """Start date too early """ + start_date = "07/20/2019" + end_date = "08/02/2022" + with pytest.raises(AssertionError): + df_num, df_den = pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date)) + + """End date too late """ + start_date = "07/20/2020" + end_date = "08/02/9999" + with pytest.raises(AssertionError): + pull_lags_data(cache_dir, lags, pd.to_datetime(start_date), pd.to_datetime(end_date)) + + diff --git a/chng_flags/tests/test_run.py b/chng_flags/tests/test_run.py new file mode 100644 index 000000000..08ac51bdc --- /dev/null +++ b/chng_flags/tests/test_run.py @@ -0,0 +1,53 @@ +# """Tests for running files.""" + +import pandas as pd +from pandas.testing import assert_frame_equal +import os +from delphi_chng_flags.run import ( + run_module) +import pytest +import shutil + +def test_output_files(): + + PARAMS = { + "common": { + "export_dir": "./receiving", + "log_filename": "./receiving/logger.log" + }, + "indicator": { + "input_cache_dir": "./cache/test_cache_with_file", + "num_lags":2, + "n_train":4, + "n_test":2, + "n_valid":1, + "lags": [1, 60], + "start_date": "01/10/2022", + "end_date": "01/24/2022" + } + } +# """Tests for running the module.""" + """Tests that the output files contain the correct results of the run.""" + run_module(PARAMS) + cache = "./cache/test_cache_with_file" + assert_frame_equal(pd.read_csv(cache + '/resid_4_2.csv', header=0, parse_dates=['date']), + pd.read_csv(cache + '/resid_4_2_orig.csv', header=0, parse_dates=['date']), + check_dtype=False) + + + + assert_frame_equal(pd.read_csv(cache + '/flag_ar_4_2.csv', header=0, parse_dates=['date']), + pd.read_csv(cache + '/flag_ar_4_2_orig.csv', header=0, parse_dates=['date']), + check_dtype = False) + + assert_frame_equal(pd.read_csv(cache + '/flag_spike_4_2.csv', header=0, parse_dates=['date']), + pd.read_csv(cache + '/flag_spike_4_2_orig.csv', header=0, parse_dates=['date']), + check_dtype=False) + + + + +#TODO: Find an example of this +# def cvxpy_fail(): +# return +#