{"cells":[{"metadata":{"_uuid":"e4bbc0bbeb9dac0f687759463b02726a6d189f39"},"cell_type":"markdown","source":"This script compares the prediction result with true result."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import plotly\nimport pandas as pd\nplotly.offline.init_notebook_mode(connected=False)","execution_count":2,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"path = \"../input/my-rnn-avito/my_rnnv5val.csv\"\n\ny = pd.read_csv(\"../input/avito-demand-prediction/train.csv\", usecols=[\"deal_probability\", \"item_id\"])\ny = y.rename(columns={\"deal_probability\": \"y\"})\n\ndf = pd.read_csv(path)\ndf = df.sample(n=1000)\ndf['deal_probability'] = df['deal_probability'].clip(0.0, 1.0) \ndf = df.merge(y, how=\"left\", on=\"item_id\")\ny_ = df.y.values\nx = df[\"deal_probability\"].values\n# make trace\ndata = [\n    plotly.graph_objs.Scatter(x = x,  y = y_, mode = \"markers\"),\n    plotly.graph_objs.Scatter(x=[0,1], y=[0,1], name=\"legend2\"),\n]\n\n# define layout\nlayout = plotly.graph_objs.Layout(\n    title=\"result\",\n    xaxis=dict(title='pred'),\n    yaxis=dict(title='true'),\n    showlegend=False)\n\nfig = dict(data=data, layout=layout)\n\nplotly.offline.iplot(fig, filename=\"result\")","execution_count":4,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true,"_uuid":"b9fb5d29b4272f496116a2b8000bf2b80df3ed5f"},"cell_type":"markdown","source":"\nThe variation is very large."}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.5","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}