update to new r monitoring functions

Author: isabelizimm

vetiver/monitor.py

@@ -1,4 +1,6 @@
 import pins
+import matplotlib as plt
+import pandas as pd
 
 # def vetiver_compute_metrics(data,
 #                             date_var,
@@ -31,51 +33,99 @@
 #     )
 
 
-def vetiver_create_pin_metrics(df_metrics,
-                                date_var,
-                                board,
-                                metrics_pin_name):
+def vetiver_create_pin_metrics(board,
+                                df_metrics,
+                                metrics_pin_name,
+                                #.index = .index,
+                                overwrite = True):
 
-    date_var <- quo_name(enquo(date_var))
+#    date_var <- quo_name(enquo(date_var)) #enquo?
 
     new_metrics = df_metrics.sort()
 
-    pins.pin_write(board, new_metrics, metrics_pin_name)
-
+    new_dates = df_metrics.index.unique()
 
-def vetiver_update_pin_metrics(df_metrics,
-                                date_var,
-                                board,
-                                metrics_pin_name):
-    
-    new_dates = df_metrics[date_var].unique()
     old_metrics = pins.pin_read(board, metrics_pin_name)
-    old_metrics = vec_slice(
-        old_metrics,
-        old_metrics[date_var] not in new_dates
+    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`"
+            )
+
+    new_metrics = old_metrics + df_metrics
+    new_metrics <- vec_slice(
+        new_metrics,
+        vctrs::vec_order(new_metrics.index)
     )
-    new_metrics <- vec_sort(vctrs::vec_rbind(old_metrics, df_metrics))
 
     pins.pin_write(board, new_metrics, metrics_pin_name)
-    
-    return new_metrics
-
-def vetiver_plot_metrics(df_metrics,
-                        date_var,
-                        estimate = estimate,
-                        metric = metric,
-                        n = n):
-
-
-    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)
+
+
+
+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
+    )
+
+
+# def eval_select_one(col, data, arg, *kw, call = caller_env()):
+#     rlang::check_installed("tidyselect")
+#     check_dots_empty()
+
+#     # `col` is a quosure that has its own environment attached
+#     env = empty_env()
+
+#     loc = tidyselect::eval_select(
+#         expr = col,
+#         data = data,
+#         env = env,
+#         error_call = call
+#     )
+
+#     if (length(loc) != 1):
+#         raise ValueError("`{arg}` must specify exactly one column from `data`.")
+
+#     return loc
+
+
+# def vetiver_plot_metrics(df_metrics,
+#                         date_var,
+#                         estimate = estimate,
+#                         metric = metric,
+#                         n = n):
+
+
+#     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)