{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(loss_fn, title=None, min_val=-0.1, max_val=0.1, n_val=100):\n",
    "    x = np.linspace(min_val, max_val, n_val)\n",
    "    y = np.linspace(min_val, max_val, n_val)\n",
    "    xx, yy = np.meshgrid(x, y)\n",
    "    z = loss_fn(xx, yy)\n",
    "\n",
    "    fig = go.Figure(data=[go.Heatmap(z=z, x=x, y=y, showscale=False)])\n",
    "    fig.update_layout(title=title, width=500, height=500)\n",
    "    fig.show()\n",
    "\n",
    "def plot_surface(loss_fn, title=None, min_val=-0.1, max_val=0.1, n_val=100):\n",
    "    x = np.linspace(min_val, max_val, n_val)\n",
    "    y = np.linspace(min_val, max_val, n_val)\n",
    "    xx, yy = np.meshgrid(x, y)\n",
    "    z = loss_fn(xx, yy)\n",
    "\n",
    "    fig = go.Figure(data=[go.Surface(z=z, x=x, y=y, showscale=False)])\n",
    "    fig.update_layout(title=title, width=500, height=500)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(y_pred, target):\n",
    "    return np.abs(y_pred - target)\n",
    "\n",
    "def madl(y_pred, target):\n",
    "    return -1 * np.sign(y_pred * target) * np.abs(target)\n",
    "\n",
    "def gmadl(y_pred, target, a=2000, b=2):\n",
    "    return -1 * (1 /(1 + np.exp(-a * y_pred * target)) - 0.5) * np.abs(target)**b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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        "plotlyServerURL": "https://plot.ly"
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   "source": [
    "plot_heatmap(mae, title=\"MAE\")\n",
    "plot_heatmap(madl, title=\"MADL\")\n",
    "plot_heatmap(gmadl, title=\"GMADL\")"
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  },
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   "source": [
    "plot_surface(mae, title=\"MAE\")\n",
    "plot_surface(madl, title=\"MADL\")\n",
    "plot_surface(gmadl, title=\"GMADL\")"
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  },
  {
   "cell_type": "code",
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   "source": [
    "## Test pytorch forcasting metric implementation of GMADL\n",
    "import torch\n",
    "from ml.loss import GMADL\n",
    "\n",
    "gmadl_loss = GMADL(reduction=None, a=1000, b=2)\n",
    "x = np.linspace(-0.001, 0.001, 100)\n",
    "y = np.linspace(-0.001, 0.001, 100)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "\n",
    "z = gmadl_loss.loss(y_pred=torch.Tensor(xx), target=torch.Tensor(yy)).numpy()\n",
    "\n",
    "fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])\n",
    "fig.update_layout(title=\"GMADL\", width=500, height=500)\n",
    "fig.show()"
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  },
  {
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   "execution_count": 14,
   "metadata": {},
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   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2, subplot_titles=['MAE', 'GMADL'])\n",
    "x =  np.linspace(-0.1, 0.1, 100)\n",
    "y =  np.linspace(-0.1, 0.1, 100)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "z1 = mae(xx, yy)\n",
    "z2 = gmadl(xx, yy)\n",
    "\n",
    "fig.add_trace(go.Heatmap(z=z1, x=x, y=y, showscale=False), row=1, col=1)\n",
    "fig.add_trace(go.Heatmap(z=z2, x=x, y=y, showscale=False), row=1, col=2)\n",
    "fig.update_layout(width=800, height=400, margin=dict(l=20, r=20, t=20, b=20))\n",
    "fig['layout']['xaxis']['title']='y_predicted'\n",
    "fig['layout']['xaxis2']['title']='y_predicted'\n",
    "fig['layout']['yaxis']['title']='y_actual'\n",
    "fig['layout']['yaxis2']['title']='y_actual'\n",
    "fig.show()\n",
    "# def plot_heatmap(loss_fn, title=None, min_val=-0.1, max_val=0.1, n_val=100):\n",
    "#     x = np.linspace(min_val, max_val, n_val)\n",
    "#     y = np.linspace(min_val, max_val, n_val)\n",
    "#     \n",
    "#     z = loss_fn(xx, yy)\n",
    "\n",
    "#     fig = go.Figure(data=[go.Heatmap(z=z, x=x, y=y, showscale=False)])\n",
    "#     fig.update_layout(title=title, width=500, height=500)\n",
    "#     fig.show()"
   ]
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