评估(分类)模块无法绘制roc_df然后报错(WARNING: metrics_classification: 模型中无分类概率,无法绘制ROC曲线图及精准率-召回率图)


(Pon) #1
# 本代码由可视化策略环境自动生成 2019年11月1日 14:17
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。


# 回测引擎:初始化函数,只执行一次
def m19_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
    # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
    stock_count = 5
    # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.options['hold_days'] = 5

# 回测引擎:每日数据处理函数,每天执行一次
def m19_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    # 1. 资金分配
    # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
    # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
    is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
    cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎:准备数据,只执行一次
def m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2018-04-30',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
#   添加benchmark_前缀,可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_

# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(close, -1)
where(label > 1.02, 1, 0)
# 极值处理:用1%和99%分位的值做clip
#clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类,这里使用20个分类
#all_wbins(label, 20)

# 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征,每行一个,可以包含基础特征和衍生特征
#return_5
#return_10
#return_20
#avg_amount_0/avg_amount_5
#avg_amount_5/avg_amount_20
#rank_avg_amount_0/rank_avg_amount_5
#rank_avg_amount_5/rank_avg_amount_10
#rank_return_0
#rank_return_5
#rank_return_10
#rank_return_0/rank_return_5
#rank_return_5/rank_return_10
#pe_ttm_0
#west_netprofit_ftm_0
#shift(west_netprofit_ftm_0, 5)
#shift(west_netprofit_ftm_0, 10)
#shift(west_netprofit_ftm_0, 20)
#shift(west_netprofit_ftm_0, 30)
#shift(west_netprofit_ftm_0, 60)
#shift(west_netprofit_ftm_0, 90)
#shift(west_netprofit_ftm_0, 180)
#mean(west_netprofit_ftm_0, 5)
#mean(west_netprofit_ftm_0, 10)
#mean(west_netprofit_ftm_0, 20)
#mean(west_netprofit_ftm_0, 30)
#mean(west_netprofit_ftm_0, 60)
#mean(west_netprofit_ftm_0, 90)
#mean(west_netprofit_ftm_0, 180)
west_eps_ftm_0
shift(west_eps_ftm_0, 5)
shift(west_eps_ftm_0, 10)
shift(west_eps_ftm_0, 20)
shift(west_eps_ftm_0, 30)
shift(west_eps_ftm_0, 60)
shift(west_eps_ftm_0, 90)
shift(west_eps_ftm_0, 180)


"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=200
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m6 = M.GBDT_train.v1(
    training_ds=m13.data,
    features=m3.data,
    num_boost_round=5,
    objective='binary:logistic',
    eval_metric='auc',
    booster='gbtree',
    eta=0.1,
    gamma=0.0001,
    _lambda=0,
    lambda_bias=0,
    alpha=0,
    max_depth=6,
    max_leaf_nodes=30,
    subsample=0.8,
    xgb_param={}
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-05-01'),
    end_date=T.live_run_param('trading_date', '2019-10-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=200
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m5 = M.advanced_auto_labeler.v2(
    instruments=m9.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀,可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(close, -1)
where(label > 1.02, 1, 0)
# 极值处理:用1%和99%分位的值做clip
#clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类,这里使用20个分类
#all_wbins(label, 20)

# 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True,
    user_functions={}
)

m10 = M.join.v3(
    data1=m5.data,
    data2=m18.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m14 = M.dropnan.v1(
    input_data=m10.data
)

m8 = M.GBDT_predict.v1(
    model=m6.model,
    data=m14.data,
    date_col='date',
    instrument_col='instrument',
    sort=True
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

m11 = M.join.v3(
    data1=m5.data,
    data2=m8.predictions,
    on='date,instrument',
    how='inner',
    sort=False
)

m12 = M.metrics_regression.v1(
    predictions=m11.data,
    explained_variance_score=True,
    mean_absolute_error=True,
    mean_squared_error=True,
    mean_squared_log_error=True,
    median_absolute_error=True,
    r2_score=True
)

m4 = M.metrics_classification.v1(
    predictions=m11.data
)

(达达) #2

参考此贴[评估(分类)模块报错计算不了roc_df,不知道是啥原因]