rewrite compute_metrics

Author: isabelizimm

vetiver/__init__.py

@@ -18,4 +18,5 @@
 from .handlers.base import VetiverHandler  # noqa
 from .handlers.sklearn import SKLearnHandler  # noqa
 from .handlers.torch import TorchHandler  # noqa
-from .rsconnect import deploy_rsconnect
+from .rsconnect import deploy_rsconnect # noqa
+from .monitor import compute_metrics, pin_metrics, plot_metrics # noqa

vetiver/monitor.py

@@ -1,131 +1,97 @@
 import pins
-import matplotlib as plt
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
 import pandas as pd
 
-# def vetiver_compute_metrics(data,
-#                             date_var,
-#                             .period,
-#                             truth, estimate, **kw,
-#                             metric_set = yardstick::metrics,
-#                             .every = 1L,
-#                             .origin = NULL,
-#                             .before = 0L,
-#                             .after = 0L,
-#                             .complete = FALSE):
-
-#     rlang::check_installed("slider")
-#     metrics_dots = list2(...)
-#     date_var = enquo(date_var)
-#     slider::slide_period_dfr(
-#         data,
-#         .i = data[[quo_name(date_var)]],
-#         .period = .period,
-#         .f = ~ tibble::tibble(
-#             !!date_var := min(.x[[quo_name(date_var)]]),
-#             n = nrow(.x),
-#             metric_set(.x, {{truth}}, {{estimate}}, !!!metrics_dots)
-#         ),
-#         .every = .every,
-#         .origin = .origin,
-#         .before = .before,
-#         .after = .after,
-#         .complete = .complete
-#     )
-
-
-def vetiver_create_pin_metrics(board,
-                                df_metrics,
-                                metrics_pin_name,
-                                #.index = .index,
-                                overwrite = True):
+def compute_metrics(data, 
+                    date_var, 
+                    period,
+                    metric_set, 
+                    truth, 
+                    pred):
 
-#    date_var <- quo_name(enquo(date_var)) #enquo?
-
-    new_metrics = df_metrics.sort()
-
-    new_dates = df_metrics.index.unique()
-
-    old_metrics = pins.pin_read(board, metrics_pin_name)
-    overlapping_dates = old_metrics.index in new_dates
-
-    if overwrite is True:
-        old_metrics = old_metrics not in overlapping_dates
-    else:
-        if overlapping_dates:
-            raise ValueError(f"The new metrics overlap with dates \
-                     already stored in {repr(metrics_pin_name)} \
-                     Check the aggregated dates or use `overwrite = True`"
+    output = pd.DataFrame()
+    rol = data.set_index(date_var)
+    rol = rol.rolling(window=period)
+
+    for metric in metric_set:
+        rol.apply(
+            _compute, 
+            raw=False, 
+            kwargs = {'df':output,
+            'truth': truth,
+            'pred': pred,
+            'metric': metric}
             )
+    
+    return output
+
+def _compute(window, 
+            df, 
+            truth, 
+            pred, 
+            metric):
+    output = metric(y_true=window, 
+                    # TO DO: change y pred to correct piece
+                    y_pred=np.random.randint(0,1000,size=(len(window),1)))
+    df.loc[window.index.max(), metric.__name__] = output
+    return output
+
+
+# def pin_metrics(board,
+#                                 df_metrics,
+#                                 metrics_pin_name,
+#                                 #.index = .index,
+#                                 overwrite = True):
+    
+# #    date_var <- quo_name(enquo(date_var)) #enquo?
 
-    new_metrics = old_metrics + df_metrics
-    new_metrics <- vec_slice(
-        new_metrics,
-        vctrs::vec_order(new_metrics.index)
-    )
-
-    pins.pin_write(board, new_metrics, metrics_pin_name)
-
-
-
-def compute_metrics(data, date_var,
-                            metric_set,
-                            truth_quo,
-                            estimate_quo,
-                            *kw):
-    index = data.date_var
-    index = min(index)
-
-    n = len(data)
-
-    metrics = metric_set(
-        data = data,
-        truth = truth_quo,
-        estimate = estimate_quo
-    )
-
-    tibble::tibble(
-        .index = index,
-        .n = n,
-        metrics
-    )
+#     new_metrics = df_metrics.sort()
 
+#     new_dates = df_metrics.index.unique()
 
-# def eval_select_one(col, data, arg, *kw, call = caller_env()):
-#     rlang::check_installed("tidyselect")
-#     check_dots_empty()
+#     old_metrics = pins.pin_read(board, metrics_pin_name)
+#     overlapping_dates = old_metrics.index in new_dates
 
-#     # `col` is a quosure that has its own environment attached
-#     env = empty_env()
+#     if overwrite is True:
+#         old_metrics = old_metrics not in overlapping_dates
+#     else:
+#         if overlapping_dates:
+#             raise ValueError(f"The new metrics overlap with dates \
+#                      already stored in {repr(metrics_pin_name)} \
+#                      Check the aggregated dates or use `overwrite = True`"
+#             )
 
-#     loc = tidyselect::eval_select(
-#         expr = col,
-#         data = data,
-#         env = env,
-#         error_call = call
+#     new_metrics = old_metrics + df_metrics
+#     new_metrics <- vec_slice(
+#         new_metrics,
+#         vctrs::vec_order(new_metrics.index)
 #     )
 
-#     if (length(loc) != 1):
-#         raise ValueError("`{arg}` must specify exactly one column from `data`.")
+#     pins.pin_write(board, new_metrics, metrics_pin_name)
 
-#     return loc
 
+def vetiver_plot_metrics(df_metrics,
+                        date_var,
+                        metric):
 
-# def vetiver_plot_metrics(df_metrics,
-#                         date_var,
-#                         estimate = estimate,
-#                         metric = metric,
-#                         n = n):
+    ncols = 1
+    nrows = len(metric)
 
+    fig, axes = plt.subplots(
+        nrows=nrows,
+        ncols=ncols
+    )
 
-#     plt.plot(x = df_metrics, y = date_var, marker=".")
-
-#     ggplot2::ggplot(data = df_metrics,
-#                     ggplot2::aes({{ date_var }}, {{.estimate}})) +
-#         # ggplot2::geom_line(ggplot2::aes(color = !!.metric), alpha = 0.7) +
-#         # ggplot2::geom_point(ggplot2::aes(color = !!.metric,
-#         #                                  size = {{n}}),
-#         #                     alpha = 0.9) +
-#         ggplot2::facet_wrap(ggplot2::vars(!!.metric),
-#                             scales = "free_y", ncol = 1) +
-#         ggplot2::guides(color = "none") +
-#         ggplot2::labs(x = NULL, y = NULL)
+    # loop for plotting each column
+    for i, col in enumerate(df_metrics.columns):
+        sns.lineplot(
+            x=df_metrics[date_var], 
+            y=df_metrics[col], 
+            ax=axes[i], 
+            color='royalblue'
+            )\
+            .set_title(col)
+
+    fig.tight_layout()