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ofranconofrancon
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Merge pull request #17 from leaf-ai/plotly
Plotly
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‎requirements.txt‎

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@@ -4,3 +4,4 @@ notebook==6.1.4
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scikit-learn==0.23.2
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tensorflow==2.3.0
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keras==2.4.3
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plotly==4.9.0

‎robojudge.ipynb‎

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@@ -324,7 +324,21 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"actual_df.head()"
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"actual_df.head(8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# actual_as_pred_df = actual_df.copy()\n",
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"# actual_as_pred_df[\"PredictorName\"] = \"Ground truth\"\n",
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"# actual_as_pred_df[\"Prediction\"] = False\n",
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"# actual_as_pred_df = actual_as_pred_df.rename(columns={\"ActualDailyNewCases\": \"PredictedDailyNewCases\",\n",
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"# \"ActualDailyNewCases7DMA\": \"PredictedDailyNewCases7DMA\"})\n",
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"# actual_as_pred_df.head(8)"
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]
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},
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{
@@ -575,6 +589,201 @@
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"cr_df[(cr_df.CountryName.isin(NORTH_AMERICA)) & (cr_df.RegionName == \"\")]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Plots"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"default_country = \"Italy\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prediction vs actual"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"country_df = ranking_df[ranking_df.CountryName == default_country]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictor_names = list(country_df.PredictorName.unique())\n",
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"country_names = list(ranking_df.CountryName.unique())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"country_df[country_df[\"PredictorName\"] == 'Predictor #27']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import plotly.graph_objects as go\n",
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"\n",
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"fig = go.Figure(layout=dict(title=dict(text=f'Predicted New Cases 7-day Moving Average in {default_country}',\n",
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" y=0.9,\n",
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" x=0.5,\n",
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" xanchor='center',\n",
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" yanchor='top'\n",
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" ),\n",
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" plot_bgcolor='#f2f2f2',\n",
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" xaxis_title=\"Date\",\n",
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" yaxis_title=\"New Cases\"\n",
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" ))\n",
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"\n",
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"# Add 1 trace per predictor\n",
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"for predictor_name in predictor_names:\n",
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" pred_country_df = country_df[country_df[\"PredictorName\"] == predictor_name]\n",
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" fig.add_trace(go.Scatter(x=pred_country_df.Date,\n",
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" y=pred_country_df.PredictedDailyNewCases7DMA,\n",
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" name=predictor_name)\n",
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" )\n",
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"\n",
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"# Add 1 trace for the true number of cases\n",
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"country_actual_df = actual_df[(actual_df.CountryName == default_country) &\n",
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" (actual_df.Date >= start_date)]\n",
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"fig.add_trace(go.Scatter(x=country_actual_df.Date,\n",
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" y=country_actual_df.ActualDailyNewCases7DMA,\n",
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" name=\"Ground Truth\",\n",
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" line=dict(color='orange', width=4, dash='dash'))\n",
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" )\n",
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"# Format x axis\n",
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"fig.update_xaxes(\n",
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"dtick=\"D1\", # Means 1 day\n",
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"tickformat=\"%d\\n%b\")\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Filter by country"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import plotly.graph_objects as go\n",
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"\n",
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"fig = go.Figure(layout=dict(title=dict(text=f'Predicted 7-day Moving Average of New Cases in {default_country}',\n",
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" y=0.9,\n",
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" x=0.5,\n",
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" xanchor='center',\n",
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" yanchor='top'\n",
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" ),\n",
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" plot_bgcolor='#f2f2f2',\n",
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" xaxis_title=\"Date\",\n",
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" yaxis_title=\"New Cases\"\n",
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" ))\n",
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"\n",
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"# Keep track of trace visibility by country name\n",
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"country_plot_names = []\n",
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"\n",
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"# Add 1 trace per predictor, per country\n",
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"for predictor_name in predictor_names:\n",
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" for country_name in country_names:\n",
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" country_df = ranking_df[ranking_df.CountryName == country_name]\n",
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" pred_country_df = country_df[country_df[\"PredictorName\"] == predictor_name]\n",
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" fig.add_trace(go.Scatter(x=pred_country_df.Date,\n",
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" y=pred_country_df.PredictedDailyNewCases7DMA,\n",
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" name=predictor_name,\n",
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" visible= (country_name == default_country))\n",
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" )\n",
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" country_plot_names.append(country_name)\n",
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"\n",
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"# For each country\n",
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"# Add 1 trace for the true number of cases\n",
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"for country_name in country_names:\n",
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" country_actual_df = actual_df[(actual_df.CountryName == country_name) &\n",
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" (actual_df.Date >= start_date)]\n",
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" fig.add_trace(go.Scatter(x=country_actual_df.Date,\n",
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" y=country_actual_df.ActualDailyNewCases7DMA,\n",
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" name=\"Ground Truth\",\n",
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" visible= (country_name == default_country),\n",
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" line=dict(color='orange', width=4, dash='dash'))\n",
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" )\n",
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" country_plot_names.append(country_name)\n",
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"\n",
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"# Format x axis\n",
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"fig.update_xaxes(\n",
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"dtick=\"D1\", # Means 1 day\n",
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"tickformat=\"%d\\n%b\")\n",
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"\n",
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"# Filter\n",
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"buttons=[]\n",
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"for country_name in country_names:\n",
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" buttons.append(dict(method='update',\n",
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" label=country_name,\n",
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" args = [{'visible': [country_name==r for r in country_plot_names]},\n",
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" {'title': \"Predicted 7-day Moving Average of New Cases in \" + country_name}]))\n",
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"fig.update_layout(showlegend=True,\n",
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" updatemenus=[{\"buttons\": buttons,\n",
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" \"direction\": \"down\",\n",
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" \"active\": country_names.index(default_country),\n",
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" \"showactive\": True,\n",
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" \"x\": 0.1,\n",
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" \"y\": 1.15}])\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Daily diff in 7 days moving average"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = px.line(ranking_df[ranking_df.CountryName == \"Italy\"], x=\"Date\", y=\"Diff7DMA\", color='PredictorName')\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,

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