Move copy test to test_diagnostics

R/tests/testthat/test_diagnostics.R

@@ -4,8 +4,9 @@ context("Prophet diagnostics tests")
 ## Makes R CMD CHECK happy due to dplyr syntax below
 globalVariables(c("y", "yhat"))
 
-DATA <- head(read.csv('data.csv'), 100)
-DATA$ds <- as.Date(DATA$ds)
+DATA_all <- read.csv('data.csv')
+DATA_all$ds <- as.Date(DATA_all$ds)
+DATA <- head(DATA_all, 100)
 
 test_that("simulated_historical_forecasts", {
   skip_if_not(Sys.getenv('R_ARCH') != '/i386')
@@ -132,3 +133,72 @@ test_that("performance_metrics", {
     sort(colnames(df_horizon)) == sort(c('coverage', 'mse', 'horizon'))
   ))
 })
+
+test_that("copy", {
+  skip_if_not(Sys.getenv('R_ARCH') != '/i386')
+  df <- DATA_all
+  df$cap <- 200.
+  df$binary_feature <- c(rep(0, 255), rep(1, 255))
+  inputs <- list(
+    growth = c('linear', 'logistic'),
+    yearly.seasonality = c(TRUE, FALSE),
+    weekly.seasonality = c(TRUE, FALSE),
+    daily.seasonality = c(TRUE, FALSE),
+    holidays = c('null', 'insert_dataframe')
+  )
+  products <- expand.grid(inputs)
+  for (i in 1:length(products)) {
+    if (products$holidays[i] == 'insert_dataframe') {
+      holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))
+    } else {
+      holidays <- NULL
+    }
+    m1 <- prophet(
+      growth = as.character(products$growth[i]),
+      changepoints = NULL,
+      n.changepoints = 3,
+      yearly.seasonality = products$yearly.seasonality[i],
+      weekly.seasonality = products$weekly.seasonality[i],
+      daily.seasonality = products$daily.seasonality[i],
+      holidays = holidays,
+      seasonality.prior.scale = 1.1,
+      holidays.prior.scale = 1.1,
+      changepoints.prior.scale = 0.1,
+      mcmc.samples = 100,
+      interval.width = 0.9,
+      uncertainty.samples = 200,
+      fit = FALSE
+    )
+    out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)
+    m1 <- out$m
+    m1$history <- out$df
+    m1 <- prophet:::set_auto_seasonalities(m1)
+    m2 <- prophet:::prophet_copy(m1)
+    # Values should be copied correctly
+    args <- c('growth', 'changepoints', 'n.changepoints', 'holidays',
+              'seasonality.prior.scale', 'holidays.prior.scale',
+              'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
+              'uncertainty.samples')
+    for (arg in args) {
+      expect_equal(m1[[arg]], m2[[arg]])
+    }
+    expect_equal(FALSE, m2$yearly.seasonality)
+    expect_equal(FALSE, m2$weekly.seasonality)
+    expect_equal(FALSE, m2$daily.seasonality)
+    expect_equal(m1$yearly.seasonality, 'yearly' %in% names(m2$seasonalities))
+    expect_equal(m1$weekly.seasonality, 'weekly' %in% names(m2$seasonalities))
+    expect_equal(m1$daily.seasonality, 'daily' %in% names(m2$seasonalities))
+  }
+  # Check for cutoff and custom seasonality and extra regressors
+  changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')
+  cutoff <- as.Date('2012-07-25')
+  m1 <- prophet(changepoints = changepoints)
+  m1 <- add_seasonality(m1, 'custom', 10, 5)
+  m1 <- add_regressor(m1, 'binary_feature')
+  m1 <- fit.prophet(m1, df)
+  m2 <- prophet:::prophet_copy(m1, cutoff)
+  changepoints <- changepoints[changepoints <= cutoff]
+  expect_equal(prophet:::set_date(changepoints), m2$changepoints)
+  expect_true('custom' %in% names(m2$seasonalities))
+  expect_true('binary_feature' %in% names(m2$extra_regressors))
+})

R/tests/testthat/test_metrics.R

@@ -1,60 +0,0 @@
-library(prophet)
-context("Prophet metrics tests")
-
-## Makes R CMD CHECK happy due to dplyr syntax below
-globalVariables(c("y", "yhat"))
-
-DATA <- head(read.csv('data.csv'), 100)
-DATA$ds <- as.Date(DATA$ds)
-
-test_that("metrics_tests_using_model", {
-  # Create dummy model
-  m <- prophet(DATA)
-  # Create metric data
-  forecast <- predict(m, NULL)
-  df <- na.omit(dplyr::inner_join(m$history, forecast, by="ds"))
-  # Check all metrics wether it is equal to its definition
-  y <- df$y
-  yhat <- df$yhat
-  expect_equal(me(m), mean(y-yhat))
-  expect_equal(mse(m), mean((y-yhat)^2))
-  expect_equal(rmse(m), sqrt(mean((y-yhat)^2)))
-  expect_equal(mae(m), mean(abs(y-yhat)))
-  expect_equal(mpe(m), 100*mean((y-yhat)/y))
-  expect_equal(mape(m), 100*mean(abs((y-yhat)/y)))
-  answer <- data.frame(
-    me=me(m),
-    mse=mse(m),
-    rmse=rmse(m),
-    mae=mae(m),
-    mpe=mpe(m),
-    mape=mape(m)
-  )
-  expect_equal(all_metrics(m), answer)
-})
-
-test_that("metrics_tests_using_simulated_historical_forecast", {
-  #skip_if_not(Sys.getenv('R_ARCH') != '/i386')
-  # Create dummy model
-  m <- prophet(DATA)
-  # Run simulated historical forecast
-  df <- simulated_historical_forecasts(m, horizon = 3, units = 'days', k = 2, period = 3)
-  # Check all metrics wether it is equal to its definition
-  y <- df$y
-  yhat <- df$yhat
-  expect_equal(me(df=df), mean(y-yhat))
-  expect_equal(mse(df=df), mean((y-yhat)^2))
-  expect_equal(rmse(df=df), sqrt(mean((y-yhat)^2)))
-  expect_equal(mae(df=df), mean(abs(y-yhat)))
-  expect_equal(mpe(df=df), 100*mean((y-yhat)/y))
-  expect_equal(mape(df=df), 100*mean(abs((y-yhat)/y)))
-  answer <- data.frame(
-    me=me(df=df),
-    mse=mse(df=df),
-    rmse=rmse(df=df),
-    mae=mae(df=df),
-    mpe=mpe(df=df),
-    mape=mape(df=df)
-  )
-  expect_equal(all_metrics(df=df), answer)
-})

R/tests/testthat/test_prophet.R

@@ -509,72 +509,3 @@ test_that("added_regressors", {
   m <- add_regressor(m, 'constant_feature')
   expect_error(fit.prophet(m, df))
 })
-
-test_that("copy", {
-  skip_if_not(Sys.getenv('R_ARCH') != '/i386')
-  df <- DATA
-  df$cap <- 200.
-  df$binary_feature <- c(rep(0, 255), rep(1, 255))
-  inputs <- list(
-    growth = c('linear', 'logistic'),
-    yearly.seasonality = c(TRUE, FALSE),
-    weekly.seasonality = c(TRUE, FALSE),
-    daily.seasonality = c(TRUE, FALSE),
-    holidays = c('null', 'insert_dataframe')
-  )
-  products <- expand.grid(inputs)
-  for (i in 1:length(products)) {
-    if (products$holidays[i] == 'insert_dataframe') {
-      holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))
-    } else {
-      holidays <- NULL
-    }
-    m1 <- prophet(
-      growth = as.character(products$growth[i]),
-      changepoints = NULL,
-      n.changepoints = 3,
-      yearly.seasonality = products$yearly.seasonality[i],
-      weekly.seasonality = products$weekly.seasonality[i],
-      daily.seasonality = products$daily.seasonality[i],
-      holidays = holidays,
-      seasonality.prior.scale = 1.1,
-      holidays.prior.scale = 1.1,
-      changepoints.prior.scale = 0.1,
-      mcmc.samples = 100,
-      interval.width = 0.9,
-      uncertainty.samples = 200,
-      fit = FALSE
-    )
-    out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)
-    m1 <- out$m
-    m1$history <- out$df
-    m1 <- prophet:::set_auto_seasonalities(m1)
-    m2 <- prophet:::prophet_copy(m1)
-    # Values should be copied correctly
-    args <- c('growth', 'changepoints', 'n.changepoints', 'holidays',
-              'seasonality.prior.scale', 'holidays.prior.scale',
-              'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
-              'uncertainty.samples')
-    for (arg in args) {
-      expect_equal(m1[[arg]], m2[[arg]])
-    }
-    expect_equal(FALSE, m2$yearly.seasonality)
-    expect_equal(FALSE, m2$weekly.seasonality)
-    expect_equal(FALSE, m2$daily.seasonality)
-    expect_equal(m1$yearly.seasonality, 'yearly' %in% names(m2$seasonalities))
-    expect_equal(m1$weekly.seasonality, 'weekly' %in% names(m2$seasonalities))
-    expect_equal(m1$daily.seasonality, 'daily' %in% names(m2$seasonalities))
-  }
-  # Check for cutoff and custom seasonality and extra regressors
-  changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')
-  cutoff <- as.Date('2012-07-25')
-  m1 <- prophet(changepoints = changepoints)
-  m1 <- add_seasonality(m1, 'custom', 10, 5)
-  m1 <- add_regressor(m1, 'binary_feature')
-  m1 <- fit.prophet(m1, df)
-  m2 <- prophet:::prophet_copy(m1, cutoff)
-  changepoints <- changepoints[changepoints <= cutoff]
-  expect_equal(prophet:::set_date(changepoints), m2$changepoints)
-  expect_true('custom' %in% names(m2$seasonalities))
-  expect_true('binary_feature' %in% names(m2$extra_regressors))
-})

python/fbprophet/tests/test_diagnostics.py

@@ -10,6 +10,8 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
+import itertools
+
 import numpy as np
 import pandas as pd
 
@@ -19,9 +21,10 @@ from unittest import TestCase
 from fbprophet import Prophet
 from fbprophet import diagnostics
 
-DATA = pd.read_csv(
+DATA_all = pd.read_csv(
     os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
-).head(100)
+)
+DATA = DATA_all.head(100)
 # fb-block 1 end
 # fb-block 2
 
@@ -165,3 +168,68 @@ class TestDiagnostics(TestCase):
             set(df_horizon.columns),
             {'coverage', 'mse', 'horizon'},
         )
+
+    def test_copy(self):
+        df = DATA_all.copy()
+        df['cap'] = 200.
+        df['binary_feature'] = [0] * 255 + [1] * 255
+        # These values are created except for its default values
+        holiday = pd.DataFrame(
+            {'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
+        products = itertools.product(
+            ['linear', 'logistic'],  # growth
+            [None, pd.to_datetime(['2016-12-25'])],  # changepoints
+            [3],  # n_changepoints
+            [True, False],  # yearly_seasonality
+            [True, False],  # weekly_seasonality
+            [True, False],  # daily_seasonality
+            [None, holiday],  # holidays
+            [1.1],  # seasonality_prior_scale
+            [1.1],  # holidays_prior_scale
+            [0.1],  # changepoint_prior_scale
+            [100],  # mcmc_samples
+            [0.9],  # interval_width
+            [200]  # uncertainty_samples
+        )
+        # Values should be copied correctly
+        for product in products:
+            m1 = Prophet(*product)
+            m1.history = m1.setup_dataframe(
+                df.copy(), initialize_scales=True)
+            m1.set_auto_seasonalities()
+            m2 = diagnostics.prophet_copy(m1)
+            self.assertEqual(m1.growth, m2.growth)
+            self.assertEqual(m1.n_changepoints, m2.n_changepoints)
+            self.assertEqual(m1.changepoints, m2.changepoints)
+            self.assertEqual(False, m2.yearly_seasonality)
+            self.assertEqual(False, m2.weekly_seasonality)
+            self.assertEqual(False, m2.daily_seasonality)
+            self.assertEqual(
+                m1.yearly_seasonality, 'yearly' in m2.seasonalities)
+            self.assertEqual(
+                m1.weekly_seasonality, 'weekly' in m2.seasonalities)
+            self.assertEqual(
+                m1.daily_seasonality, 'daily' in m2.seasonalities)
+            if m1.holidays is None:
+                self.assertEqual(m1.holidays, m2.holidays)
+            else:
+                self.assertTrue((m1.holidays == m2.holidays).values.all())
+            self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
+            self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
+            self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
+            self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
+            self.assertEqual(m1.interval_width, m2.interval_width)
+            self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
+
+        # Check for cutoff and custom seasonality and extra regressors
+        changepoints = pd.date_range('2012-06-15', '2012-09-15')
+        cutoff = pd.Timestamp('2012-07-25')
+        m1 = Prophet(changepoints=changepoints)
+        m1.add_seasonality('custom', 10, 5)
+        m1.add_regressor('binary_feature')
+        m1.fit(df)
+        m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
+        changepoints = changepoints[changepoints <= cutoff]
+        self.assertTrue((changepoints == m2.changepoints).all())
+        self.assertTrue('custom' in m2.seasonalities)
+        self.assertTrue('binary_feature' in m2.extra_regressors)

python/fbprophet/tests/test_prophet.py

@@ -10,7 +10,6 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import itertools
 import numpy as np
 import pandas as pd
 
@@ -545,68 +544,3 @@ class TestProphet(TestCase):
         m.add_regressor('constant_feature')
         with self.assertRaises(ValueError):
             m.fit(df.copy())
-
-    def test_copy(self):
-        df = DATA.copy()
-        df['cap'] = 200.
-        df['binary_feature'] = [0] * 255 + [1] * 255
-        # These values are created except for its default values
-        holiday = pd.DataFrame(
-            {'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
-        products = itertools.product(
-            ['linear', 'logistic'],  # growth
-            [None, pd.to_datetime(['2016-12-25'])],  # changepoints
-            [3],  # n_changepoints
-            [True, False],  # yearly_seasonality
-            [True, False],  # weekly_seasonality
-            [True, False],  # daily_seasonality
-            [None, holiday],  # holidays
-            [1.1],  # seasonality_prior_scale
-            [1.1],  # holidays_prior_scale
-            [0.1],  # changepoint_prior_scale
-            [100],  # mcmc_samples
-            [0.9],  # interval_width
-            [200]  # uncertainty_samples
-        )
-        # Values should be copied correctly
-        for product in products:
-            m1 = Prophet(*product)
-            m1.history = m1.setup_dataframe(
-                df.copy(), initialize_scales=True)
-            m1.set_auto_seasonalities()
-            m2 = m1.copy()
-            self.assertEqual(m1.growth, m2.growth)
-            self.assertEqual(m1.n_changepoints, m2.n_changepoints)
-            self.assertEqual(m1.changepoints, m2.changepoints)
-            self.assertEqual(False, m2.yearly_seasonality)
-            self.assertEqual(False, m2.weekly_seasonality)
-            self.assertEqual(False, m2.daily_seasonality)
-            self.assertEqual(
-                m1.yearly_seasonality, 'yearly' in m2.seasonalities)
-            self.assertEqual(
-                m1.weekly_seasonality, 'weekly' in m2.seasonalities)
-            self.assertEqual(
-                m1.daily_seasonality, 'daily' in m2.seasonalities)
-            if m1.holidays is None:
-                self.assertEqual(m1.holidays, m2.holidays)
-            else:
-                self.assertTrue((m1.holidays == m2.holidays).values.all())
-            self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
-            self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
-            self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
-            self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
-            self.assertEqual(m1.interval_width, m2.interval_width)
-            self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
-
-        # Check for cutoff and custom seasonality and extra regressors
-        changepoints = pd.date_range('2012-06-15', '2012-09-15')
-        cutoff = pd.Timestamp('2012-07-25')
-        m1 = Prophet(changepoints=changepoints)
-        m1.add_seasonality('custom', 10, 5)
-        m1.add_regressor('binary_feature')
-        m1.fit(df)
-        m2 = m1.copy(cutoff=cutoff)
-        changepoints = changepoints[changepoints <= cutoff]
-        self.assertTrue((changepoints == m2.changepoints).all())
-        self.assertTrue('custom' in m2.seasonalities)
-        self.assertTrue('binary_feature' in m2.extra_regressors)